semvec/pypi-packages huggingface dataset:
smappi 0.1.7
Python Client for Smappi API
License: MIT
Keywords: smappi

Python Client for Smappi API
=============================

Support python>=2.7, python>=3.0

.. image:: https://secure.travis-ci.org/smappi/smappi-py.png
    :target: http://travis-ci.org/smappi/smappi-py

More information on https://smappi.org/

Installation
-------------

This client is delivered without any dependencies!

.. code:: bash

   pip install smappi


Examples
--------

Use the helper:

.. code:: python

   >>> from smappi import smappi
   >>> smappi('adw0rd/example').greeting(name='friend')
   "Hello, friend!"

Or use the full version:

.. code:: python

   >>> import smappi
   >>> smappi.Request('adw0rd/example', 'json').calculator(digits=[42, 42, 42])
   126
   >>> smappi.Request('adw0rd/example', 'json').calculator(digits='42,42,42')
   126
   
For local development and debug specify host as path:

.. code:: python

    >>> from smappi import smappi
    >>> smappi('localhost:8000').greeting(name='friend')
    "Hello, friend!"
tab2latex 0.1.7
A small Python package that puts your numpy arrays into latex files using the table/longtable environment
License: MIT


If you got a problem or suggestions to improve the scripts, please create an [issue in git](https://github.com/Julian-Hochhaus/py_modules/issues).

Using **textable**:

    textable.latex_tab(data=[arr1,arr2],names=[r"title column 1",r"title column 2"], filename=r"example.tex",caption=r"Beautiful caption",label=r"important_label",dec_points=[2,0])


Where data_array must be an array of arrays; names_array must be an array of strings, containing the column-names, filename has to be a string,too.

dec_points sets precision for each column. dec_points is ignored, if uncertainty-arrays are used as input. Instead, the rounding rules by Particle Data Group are used (as in uncertainties-package implemented)



For an example, have a look at example.py

label must be a string.


To write long tables, use

    textable.long_tab(data=[arr1,arr2],names=[r"title column 1",r"title column 2"], filename=r"example.tex",caption=r"Beautiful caption",label=r"important_label",dec_points=[2,0])



**Btw: You do not need to add prefix "tab:".**


If you want to see the results, compile the .tex-file.
To do so, easiest way is to use the makefile.
That will compile main.tex where the generated example.tex is included.

Version:
0.0.5: Fixed errors with uncertainties
perscode 0.0.1
Representation of persistence diagrams using persistence codebooks
License: MIT
Keywords: persistent homology,persistence codebooks,persistence diagrams,topological data analysis,algebraic topology

perscode
===

Vectorization methods for persistence diagrams based in the paper [Persistence Codebooks for
Topological Data Analysis](https://arxiv.org/abs/1802.04852).

## Usage

```python
import perscode
import numpy as np

# generate diagrams
diagrams = [np.random.rand(100,2) for _ in range(20)]
for diagram in diagrams:
    diagram[:,1] += diagram[:,0]

# N is the size of the vectors
# normalize is a Bool to whether or not normalize the output vector
pbow = perscode.PBoW(N = 3, normalize = False)
wpbow = perscode.wPBoW(N = 3)
# n_subsample is an int or None. If none all points will be used when calculating GMMs.
spbow = perscode.sPBoW(N = 10, n_subsample = None)

# vectorize diagrams
pbow_diagrams  = pbow.transform(diagrams)
wpbow_diagrams = wpbow.transform(diagrams)
spbow_diagrams = spbow.transform(diagrams)

# for PVLAD and stable PVLAD
pvlad = perscode.PVLAD(N = 3)
spvlad = perscode.sPVLAD(N = 3)

pvlad_diagrams = pvlad.transform(diagrams)
spvlad_diagrams = spvlad.transform(diagrams)
```

## TODO
- [x] Implement options to pass cluster centers as arguments in wPBoW and sPBoW.
- [x] Implement PVLAD
- [x] Implement sPVLAD
- [ ] Implement PFV
- [x] Implement optional weighted subsampling to wPBoW, sPBoW, sPVLAD classes.