semvec/pypi-packages huggingface dataset:
pycsk 0.2.0
Clear sky charts in your terminal
License: MIT
Requires Python: >=3.6

# PyCsk

## Clear Sky Charts in your terminal

![ClearSkyChat](https://i.imgur.com/DAZFlAC.png)

This is a program to pull stargazing conditions from [Clear Sky Charts](http://www.cleardarksky.com/csk/).

The colors will change according to when is the best stargazing forecast. For example, red will indicate that conditions are not great for that particular day, while green indicates that it will be a good day. For more information on which each sections means, see [here](http://www.cleardarksky.com/c/BdmIDkey.html?1#how).

### Requirements:

`python3` (>= 3.6)

`appdirs` (>= 1.4.3)

`sty` (>= 1.0.0b9)


### Installation

`$ pip3 install pycsk`

### Usage

`$ csk`

To search for cities by states:

`$ csk --search-by-state`

To search for a specific city:

`$ csk --search-by-city`
gtom 0.5
computes the general topological overlap matrix of an undirected network
License: MIT

General Topological Overlap Measure
===================================

This package takes an undirected unweighted scipy.sparse adjacency
matrix as an input and computes the GTOM(m) method, using m+1-step
neighbors (1). It's highly efficient and can be used for parallel
computation by calling the function for only few nodes at a time.

(1) `*Gene network interconnectedness and the generalized topological
overlap
measure* <http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.93.329&rep=rep1&type=pdf>`__,
A. M. Yip and S. Horvath, BMC Bioinformatics 2007 8:22

Install
-------

.. code:: bash

    $ pip install gtom

Example
-------

Recreating Panels a and c of Figure 3 of the technical report [1]:

.. code:: python

    #!python

    import matplotlib.pyplot as pl
    import networkx as nx
    from gtom import gtom
    import scipy.sparse as sprs
    import numpy as np

    edges = [(0,1),(1,2),(0,3),(0,4),(0,5),(0,7),
             (1,3),(1,4),(1,6),(1,8),(1,9),(1,10),
             (5,6),(7,8)]
    G = nx.Graph()
    G.add_edges_from(edges)

    pos = nx.spring_layout(G)
    labels = { n:str(n+1) for n in G.nodes()}
    nx.draw(G,pos=pos)
    nx.draw_networkx_labels(G,pos=pos,labels=labels)

    N = G.number_of_nodes()
    edges = np.array(edges,dtype=int)
    A = sprs.csc_matrix((np.ones((edges.shape[0],)),(edges[:,0],edges[:,1])),dtype=float,shape=(N,N))
    A += A.T

    print("recreate results from figure 3 in [1]")
    print("       |\t(i,j)=(1,2)\t(i,j)=(1,3)\t(i,j)=(2,3)")
    print("---------------------------------------------------------")
    for m in range(3):
        T = gtom(A,m)
        print(" m = %d |\t%f\t%f\t%f" %(m,T[0,1],T[0,2],T[1,2]))

    pl.show()