semvec/pypi-packages huggingface dataset:
spec-map-analysis 1.1.1
An astronomical spectral map analysis package
License: MIT
Requires Python: >=3.6

## General Description

**Spec_map_analysis** is a spectral map analysis package. The package contains modules that allow for the fitting of emission lines in spectral maps, resampling and convolution of images, and derivation of emission line ratios and subsequently electron densities. 

 - The emission line fitting module can fit a single emission line with a Gaussian profile and optionally a polynomial continuum component. The spectral fitting is performed independently for each spatial pixel using a non-linear least squares method.

- The resampling and convolution module resamples one image to another image's pixel resolution, and then convolves either image to the specified spatial resolution. The convolution allows for the propagation of uncertainties, assuming they are correlated, using the method presented in [Klein (2021)](https://iopscience.iop.org/article/10.3847/2515-5172/abe8df). 

- The line ratio and electron density module allow for the derivation of specified line ratios and subsequently, electron density maps using atomic data from [PyNeb](http://research.iac.es/proyecto/PyNeb//). Aperture photometry can also be performed on the line intensity maps, and then used to calculate line ratios and electron densities for aperture regions.


## Requirements

**Spec_map_analysis** has the following requirements:

 - [python](https://www.python.org) 3.6 or later
 - [numpy](https://numpy.org) 1.20 or later
 - [astropy](https://www.astropy.org) 4.2 or later
 - [scipy](https://www.scipy.org) 1.6 or later
 - [reproject](https://reproject.readthedocs.io/en/stable/#) 0.7 or later
 - [PyNeb](http://research.iac.es/proyecto/PyNeb//) 1.1 or later

**Spec_map_analysis** also has optionally dependencies on:

 - [photutils](https://photutils.readthedocs.io/en/stable/index.html) 1.1 or later: To derive aperture photometry.
 - [matplotlib](https://matplotlib.org) 3.3 or later: To plot images.
 - [emcee](https://emcee.readthedocs.io/en/stable/) 3.0 or later: To fit spectral maps using an MCMC algorithm.


## Installation

**Spec_map_analysis** can be downloaded, installed and upgraded using [pip](https://pip.pypa.io/en/latest/).

To install only the required packages, run:
```
pip install spec_map_analysis
```
To install all package dependencies, run:
```
pip install spec_map_analysis[all_dep]
```
To install directly from the [GitHub repository](https://github.com/kjdoore/spec_map_analysis), run:
```
pip install git+https://github.com/kjdoore/spec_map_analysis.git
```


## Manual

The manual can be found [here](https://github.com/kjdoore/spec_map_analysis/tree/master/docs).


## Reporting Issues

If you happen to find a bug in spec_map_analysis, please report it by creating a new issue on the [**spec_map_analysis** GitHub issue tracker](https://github.com/kjdoore/spec_map_analysis/issues). Please include an example of the issue that allows for its reproduction, so the problem can be fixed.
get-crypto-price 0.2.2
A library to getting crypto price.
License: MIT
Requires Python: >= 3


# Get Crypto Price
A library to getting crypto price.
# Install
```
pip3 install get-crypto-price
```
# Using
## In another script
```python
from get_crypto_price import get_crypto_price
# get_crypto_price(source = "bitstamp", crypto="btc", pair = "usdt")
print(get_crypto_price())
```
## In command line
```console
  -h, --help            show this help message and exit
  -s SOURCE, --source SOURCE
                        Source
  -c CRYPTO, --crypto CRYPTO
                        Crypto
  -p PAIR, --pair PAIR  Pair
```
```console
get_crypto_price
```
movieFinderIMDB 0.0.5
A moviefinder based on IMDB rating
License: MIT
Keywords: movie,imdb

Repo made because I was frustrated with IMDB's search engine or lack hereof.

``` imdb_moviefinder``` makes use of year, rating, votes and genres to find movies of your liking. Use as you please.

A clean installation using jupyter lab as interactive interpreter can be done as so:


```conda create --name imdb python=3.9```

```conda activate imdb```

```pip install movieFinderIMDB==0.0.5```

```conda install jupyterlab```

From here I'd open jupyterlab and with ```jupyter lab``` and run the code below

```
import movieFinder
movieFinder.store_movies() # this may take a while as it downloads, unpacks and compress a couple of large files
import pandas as pd
df = pd.read_pickle('all_movies.pkl')
mf = movieFinder.imdb_moviefinder(df)
movies = mf.search_movies(year_from=2015, year_to=2021, genre='Sci-Fi', num_votes_from=100000, average_rating_from=7)
movies
```
This will show the some great movies from 2015 to 2021. Note that genres hold the following possibilities: 

```
genres: ['Comedy' 'Drama' 'Action' 'Animation' 'Horror' 'Biography' 'Adventure'
 'Documentary' 'Crime' 'Fantasy' 'Thriller' 'Mystery' 'Romance' 'Family'
 'Western' 'Musical' 'Sci-Fi' 'History' 'Music' 'Sport' 'War'] 
 ```
 
 
If interest exist you can add the release date to the dataframe as done below, beware though, that this might take a few minutes as it depends on IMDB's website itself. 


```mf.get_release_month(movies)```


Change Log
==========

0.0.1 (13/08/2021)
-------------------
- First Release

0.0.2 (13/08/2021)
-------------------
- Fixed imdbpy

0.0.3 (13/08/2021)
-------------------
- Fixed setup.py requires

0.0.4 (13/08/2021)
-------------------
- Populated Readme

0.0.5 (13/08/2021)
-------------------
- Edited README