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How to replicate a matlab model for copula (copulafit) in python

I have found this simple very well-explained example of copula which is absolutely fine for my purpose.
https://it.mathworks.com/help/stats/copulafit.html
I would simply need to replicate it.
However, I cannot use Matlab but Python.
Do you know how I can replicate what's in here in python?
For example, I have tried Copulas, but for some reason I cannot visualise the copula but directly the multivariate distribution of my resampled data

In python you can use Copulas library (https://sdv.dev/Copulas/index.html)

what to do to fit gaussian model in scherrer equation in python?

i'm new to python and i'm trying to fit the gaussian function into scherrer equation using python and the problem is that i don't know how to do it . similarly with the laurentzian model . can some one explains me how to do it . Thanks
More explanation : for x and y values i want them to be read from a text file and then use them in the process.

If you want a more specific solution you should probably provide an example.
In general, scipy.curve_fit is a great solution for the most fitting problems.
You can find a tutorial about it here. In particular, there is also an example of how to fit a Gaussian function: https://scipy-cookbook.readthedocs.io/items/FittingData.html#Fitting-gaussian-shaped-data.

You might want to take a look here:
Gaussian fit for Python
I have no idea how you get your data, but if you have just the function, try generating values using the function to get something you can actually fit the gauss curve.

How to identify the distribution of the given data in Python?

I have some data and want to find the distribution that fits them well. I found one post inMATLAB and one post in r. This post talks about a method in Python. It is trying different distributions and see which one fits better. I was wondering if there is any direct way (like allfitdist() in MATLAB) in Python.

Fitter in python provides similar functionality. The code looks like:
from fitter import Fitter
f = Fitter(data)
f.fit()
For more information, please take a look at https://pypi.python.org/pypi/fitter

Can scipy.stats to calculate pmf automatically?

I have two questions.
1) I have an array like [1,2,3,4,5,5,3,1]. and I don't know which distributions it is. Can I use scipy.stats to calculate pmf,cdf automatically?
2)scipy.stats is just like a library of distributions? If I want to analysis data, I have to find one distributions or define one? I need to manually calculate some of data, like pmf. Am I understanding correctly?

Well, scipy.stats is not a library for telling you the distribution of data and calculating pmf and cdf automatically. Its a library for easing your tasks while estimating the probabily distribution. You have to explore your data and find which distribution which fits your data with least error ,which is the ultimate task and scipy.stats helps you achieving this.... you don't have to reinvent the wheel as they say by writing all the mathematical functions again and again.
Well, to answer your question in the comment, lets suppose you have a dataset, to get an insight and get a starting point for your analysis, what you'll do is plot the data in a historgram (which also shows distribution of data), now you can plot different distributions on the same plot using scipy.stats to get a feel of bestfit....
Check out this answer, it might help ya...
https://stats.stackexchange.com/questions/132652/how-to-determine-which-distribution-fits-my-data-best

PCA with missing values in Python

I'm trying to do a PCA analysis on a masked array. From what I can tell, matplotlib.mlab.PCA doesn't work if the original 2D matrix has missing values. Does anyone have recommendations for doing a PCA with missing values in Python?
Thanks.

Imputing data will skew the result in ways that might bias the PCA estimates. A better approach is to use a PPCA algorithm, which gives the same result as PCA, but in some implementations can deal with missing data more robustly.
I have found two libraries. You have
Package PPCA on PyPI, which is called PCA-magic on github
Package PyPPCA, having the same name on PyPI and github
Since the packages are in low maintenance, you might want to implement it yourself instead. The code above build on theory presented in the well quoted (and well written!) paper by Tipping and Bishop 1999. It is available on Tippings home page if you want guidance on how to implement PPCA properly.
As an aside, the sklearn implementation of PCA is actually a PPCA implementation based on TippingBishop1999, but they have not chosen to implement it in such a way that it handles missing values.
EDIT: both the libraries above had issues so I could not use them directly myself. I forked PyPPCA and bug fixed it. Available on github.

I think you will probably need to do some preprocessing of the data before doing PCA.
You can use:
sklearn.impute.SimpleImputer
https://scikit-learn.org/stable/modules/generated/sklearn.impute.SimpleImputer.html#sklearn.impute.SimpleImputer
With this function you can automatically replace the missing values for the mean, median or most frequent value. Which of this options is the best is hard to tell, it depends on many factors such as how the data looks like.
By the way, you can also use PCA using the same library with:
sklearn.decomposition.PCA
http://scikit-learn.org/stable/modules/generated/sklearn.decomposition.PCA.html
And many others statistical functions and machine learning tecniques.