semvec/pypi-packages huggingface dataset:
mendotapy 1.0.0
load Lake Mendota ice phenology
License: MIT
Requires Python: >=3.6
Keywords: mendota

# mendotapy

A Python 3.6+ package for analysis-ready [Lake Mendota ice phenology](https://climatology.nelson.wisc.edu/first-order-station-climate-data/madison-climate/lake-ice/history-of-ice-freezing-and-thawing-on-lake-mendota/) data

Installing
----------

### PyPi
```sh
pip install mendotapy==1.0.0
```

### GitHub
```sh
pip install -e git+https://github.com/lgloege/mendotapy.git#egg=mendotapy
```

Using the Package
----------
**Read data into a dataframe**
```python
import mendotapy
df = mendotapy.load()
```
outerr2html 0.11
Pipe stdout/stderr to TXT/HTML (Windows only)
License: MIT
Keywords: requests, ip, v4


# Pipe stdout/stderr to TXT/HTML (Windows only)

## Tested against Windows 10 / Python 3.11 / Anaconda

### pip install outerr2html

### pip install ansi2html (if you want HTML output too)


```PY
from outerr2html import stdout_redirector, libc, config

config.enco = "utf-8"
import sys
import os
import time

with stdout_redirector(
    outfolder="c:\\htmltestoutput",
    print_stdout=True,
    print_stderr=True,
    line_limit_out=1000, # creates a new file when limit has been reached
    line_limit_err=1000,
    sleep_at_end=2,
    convert2html=False,
    sleep_between_checks=0.1,
):
    for h in range(10):
        sys.stderr.write("stderr test\n")
        sys.stderr.flush()  # necessary
        os.system("echo console test")  # won't be captured with ipython
        libc.puts(b"C test")  # won't be captured with ipython
        libc.fflush(None)  # necessary
        print("python test ")
        print(1234)
        time.sleep(1)
print("oi")  # not captured

import pandas as pd
from PrettyColorPrinter import add_printer

add_printer(1)
df = pd.read_csv(
    r"https://github.com/datasciencedojo/datasets/raw/master/titanic.csv",
    engine="python",
    on_bad_lines="skip",
    encoding="utf-8",
    encoding_errors="ignore",
)
with stdout_redirector(
    outfolder="c:\\htmltestoutput",
    print_stdout=True,
    print_stderr=True,
    line_limit_out=1000,
    line_limit_err=1000,
    sleep_at_end=2,
    convert2html=False,
    sleep_between_checks=0.1,
):
    for h in range(10):
        sys.stderr.write("stderr test\n")
        sys.stderr.flush()  # necessary
        print(df)
        os.system("echo console test")  # won't be captured with ipython
        libc.puts(b"C test")  # won't be captured with ipython
        libc.fflush(None)  # necessary
        print("python test ")
        print(1234)
        time.sleep(1)
print("oi")  # not captured
```
msr 0.8.2
Tools to compute the minimum semidefinite rank of a simple undirected graph.
License: MIT
Requires Python: <4.0,>=3.11

# MSR: Minimum Semidefinite Rank
https://github.com/samreynoldsmath/msr

## Description
Tools to compute the minimum semidefinite rank of a simple undirected graph.

The minimum semidefinite rank of a graph $G$, denoted by $\text{msr}(G)$, is
the smallest rank of a positive semidefinite matrix $A$ such that $A$ is a
generalized adjacency matrix of $G$;
that is, $A_{ij} \neq 0$ if and only if $i \neq j$ and $ij \in E(G)$.
Equivalently, $\text{msr}(G)$ is the smallest dimension $d$ such that every
vertex $i$ of $G$ can be assigned to a vector $x_i \in \mathbb{R}^d$ such for
each $i \neq j$, we have that $x_i \cdot x_j \neq 0$ if and only if
$ij \in E(G)$. Surprisingly, the graph invariant $\text{msr}(G)$ can often be
computed only by consideration of the graph structure, without the need to
actually do any linear algebra.

## Comments
- This package began as a school project for a course on semidefinite
	programming (see the
	[final report](doc/mth610-semidefprog-final-report-reynolds.pdf)).
 - In addition to SDP, this package also uses combinatorial techniques to
	compute bounds on the MSR, some of which are well-known in the literature,
	and some of which are still under development.
 - The package uses a custom graph representation, but supports conversion
  	to\from [networkx](https://networkx.org/) graphs.
- The package is not designed with efficiency in mind, and probably will not
	scale well to large graphs.

## Dependencies
This project is written in Python 3.11 and uses the following packages:
- [cvxpy](https://www.cvxpy.org/) is used to solve semidefinite programs
- [matplotlib](https://matplotlib.org/) is used for visualization
- [networkx](https://networkx.org/) is used for graph isomorphism testing
- [tqdm](https://tqdm.github.io/) is used for progress bars
See [requirements.txt](requirements.txt) for a complete list of dependencies.

## Author
[Sam Reynolds](https://sites.google.com/view/samreynolds)
is a PhD student studying Mathematical Sciences at
[Portland State University](https://www.pdx.edu/math/).

## License
Copyright (c) 2023 under the
[MIT license](LICENSE).