semvec/pypi-packages huggingface dataset:
EZPlotly 0.0.8
An easy wrapper for making Plotly plots in Jupyter notebooks
Requires Python: >=3.6

# EZPlotly and EZPlotly_bio For Jupyter Notebooks
Introducing EZPlotly: An easy, intuitie wrapper for making Plotly plots in Jupyter notebooks

Plotly offers interactive plots, as opposed to the static plots that most other python visualization tools provide. However, Plotly syntax can be challenging to write, whereas the other libraries are a lot easier to plot with. EZPlotly helps bridge the gap. EZPlotly makes ploting with Plotly simpler and mot matplotlib or matlab-like for experienced users of those toolsets.

In addition, EZPlotly offers domain-specific extensions for making interactive domain-specific plots in Plotly.
-The YayROCS package enables common Deep Learning / Machine Learning plotting functions such as making interactive ROC curves, AUCs, and p-Value comparison charts.
-The EZPlotly_bio extension offers a rich toolset for bioinformaticians to make common bioinformatics plots such as qqplots, chromosome rolling medians, chromosome frequency histograms and barcharts. 

# Installation

pip install EZPlotly

# Example syntax:

```python
import EZPlotly as EP

exampleHist = EP.hist(data=a,min_bin=0.0,max_bin=1.0,bin_size=0.1,title='MyHistogram',xlabel='a')
exampleScatter = EP.scattergl(x=a,y=b,title='Test',xlabel='x',ylabel='y')
EP.plot_all([exampleHist,exampleScatter])
```

For more examples, checkout run the EZPlotlyExamples.ipynb in Jupyter!

# EasyPlotly_bio for Bioinformaticians:

In the bioinformatics domain? Currently in the works is a bioinformatics extension (EasyPlotly_bio) for making common bioinformatics plots such as qqplots, chromosome rolling medians, chromsome count bar charts, and chromosome histograms.
hmm4ds 0.0.6
Python code for hidden markov models
Requires Python: >=3.7

Hidden Markov Models
====================

HMM provides python3 code that implements the following algorithms
for hidden Markov models:

Forward: Recursive estimation of state probabilities at each time t,
	 given observation likelihoods for times 1 to t

Backward: Combined with Forward, provides estimates of state
	  probabilities at each time given _all_ of the observation
	  likelihoods

Train: Implements Baum Welch algorithm which finds a local maximum of
       likelihood of model parameters

Decode: Implements Viterbi algorithm for finding the most probable
	state sequence

Implementations of the above algrithms are independent of the
observation model.  HMM enables users to implement any observation
model by writing code for a class that provides methods for
calculating the likelihood of an observation given a state and for
reestimating model parameters given observations and state
likelihoods.

HMM includes implementations of the following observation models:

IntegerObservation: Integers in a finite range

Gauss: Floats with state dependent mean and variance

GaussMAP: Like Gauss but uses maximum a posteriori probability
estimation

MultivariateGaussian: Like GaussMAP but observations are vectors of
floats

AutoRegressive: Like GaussMAP but with linear autoregressive forecast
and Gaussian residual

Observation_with_bundles: Observations that can include classification data

I (Andy Fraser) restarted this project on 2021-01-22.  I will rewrite
the code for my book "Hidden Markov Models and Dynamical Systems".
This project contains general HMM code that is not specific to the
book.

You can redistribute and/or modify hmm under the terms of the GNU
General Public License as published by the Free Software Foundation,
either version 3 of the License, or (at your option) any later
version.  See the file "License" in the root directory of the
hmm distribution.