semvec/pypi-packages huggingface dataset:
gismeteoscraper 0.0.3
A simple web scraper for gismeteo.com
Requires Python: >=3.6

# GisMeteoScraper

This is a python web scraper for [gismeteo](https://www.gismeteo.com/), it scrapes all html content from webpage.


### Requirements

GisMeteoScraper uses libraries such as:
* requests
* bs4 (BeautifulSoup4)

GisMeteoScraper is also built on `python 3.7.3`

### Usage example

    from scraper import GisMeteoScraper
    
    gis = GisMeteoScraper("link to gismeteo with town")
    weather = gis.scrape_info()


### Output
GisMeteoScraper's `scrape_info()` outputs `dict()` object, which contains weather on each timeslot `as int()` *(like 0, or 21)*, for example:

    timeslot: 
    {'gm_activity': {
		    'description': '', 
		    'value': },
     'humidity': '',
       'id': ,
       'moon': {'description': '',
                'sunrise': '',
                'sunset': '',
                'title': ''},
       'percipitation': '',
       'percipitation_in_radius': '',
       'pressure': {
		       'h_pa': '', 
		       'in_hg': '', 
		       'mm_hg_atm': ''},
       'road_condition': '',
       'sun': {
		   'description': '',
               'sunrise': '',
               'sunset': '',
               'title': ''},
       'temperature': {
		       'celsius': '', 
		       'fahrenheit': ''},
       'uvb_index': {
		       'description': '', 
		       'value': },
       'visibility': '',
       'wind': {'direction': '',
                'gust': {
		                'km/h': '', 
		                'm/s': '', 
		                'mi/h': ''},
                'speed': {
		                'km/h': '', 
		                'm/s': '', 
		                'mi/h': ''}}},

Note:
> Description values *like `humidity[description]`* may contain text according to your region.
> For example `SW` (South-West) in English -> `Р®Р’` (Р®РіРѕ-Р’РѕСЃС‚РѕРє) in Russian, and so on.

But sometimes some field may have `No data` in them. That's probably because [gismeteo](https://www.gismeteo.com/) didn't show this field, or it was empty. Also, returning `dict()` may contain `_error` field if there was any.

If you have any questions or issues, you can see all issues [here](https://github.com/egordunaev/gismeteo-scraper/issues), alternatively you can [create a new issue](https://github.com/egordunaev/gismeteo-scraper/issues/new)
brendapy 0.4.1
Parsing BRENDA information
Keywords: BRENDA

brendapy: BRENDA in python
====================================

The ``brendapy`` package provides a python parser for
`BRENDA <https://www.brenda-enzymes.org/index.php>`__
enzyme information. The parser extracts the protein information from the
database flat file and makes it accessible in a simple manner.

This package was developed in the context of building kinetic pathway models
with source code at https://github.com/matthiaskoenig/brendapy

``brendapy`` support has been tested on ``py36`` and ``py37``.

If you use this package cite `BRENDA <https://www.brenda-enzymes.org/index.php>`__

::

    BRENDA in 2019: a European ELIXIR core data resource
    Jeske L., Placzek S., Schomburg I., Chang A., Schomburg D., Nucleic Acids Res., in print (2019)


© 2019 Matthias König
latlonconv 1.0.1
Methods for representing geographic coordinates
Requires Python: >=3.6

# latlonconv

A copy of the LatLon package that actually works (and does not depend on `pyproj`),
but has reduced functionality focusing on format conversion only:

## Example

```python
import latlonconv as ll

coord1 = ll.LatLon(32.30642, 122.61458)
coord2 = ll.string2latlon("""32.30642° N""", """122.61458° W""", """D%° %H""")
conv1 = coord1.to_string("""d%° %m%' %S%" %H""")
conv2 = coord2.to_string("""d%° %m%' %S%" %H""")

print(conv1)
print(conv2)
```

## Format string syntax

latlonconv.string2latlon and latlonconv.LatLon.to_string both take a formatter string which is loosely modeled on the format keyword used in datetime’s strftime function. Indicator characters (e.g. H or D) are placed between a specific separator character (%) to specify the way in which a coordinate string is formatted. Possible values are as follows:

> H is a hemisphere identifier (e.g. N, S, E or W)
>
> D is a coordinate in decimal degrees notation (e.g. 5.833)
>
> d is a coordinate in degrees notation (e.g. 5)
>
> M is a coordinate in decimal minutes notation (e.g. 54.35)
>
> m is a coordinate in minutes notation (e.g. 54)
>
> S is a coordinate in seconds notation (e.g. 28.93)
>
> Any other characters (e.g. ‘ ‘ or ‘, ‘) will be treated as a separator between the above components.
>
> All components should be separated by the % character. For example, if the coord_str is ‘5, 52, 59.88_N’, the format_str would be ‘d%, %m%, %S%_%H’
aprioripy 0.0.2
Apriori algorithm.
Requires Python: >=3.6

# Aprioripy: Apriori algorithm.
<img src="4742c1dc-c672-4194-96c3-54ee5b88a840.jpeg">

## Apriori algorithm usage:

```python
from apriopy import Apriopy


test_table = pd.DataFrame({
    "items": ["1, 3, 4", "2, 3, 5", "1, 2, 3, 5", "2, 5"]
})

print("Test table")
print(test_table)

ap = Aprioripy(table=test_table)

print("\nFrequency table:")
print(ap.frequency_table)

ap.apriori(min_support=0.5)

for i in ap.association_tables.keys():
    print("\nAssociation table " + i)
    print(ap.association_tables[i])

test_table = pd.DataFrame(
    [
        {"1": 1, "2": 0, "3": 1, "4": 1, "5": 0},
        {"1": 0, "2": 1, "3": 1, "4": 0, "5": 1},
        {"1": 1, "2": 1, "3": 1, "4": 0, "5": 1},
        {"1": 0, "2": 1, "3": 0, "4": 0, "5": 1}
    ]
)

print("\nTest table:")
print(test_table)

ap = Aprioripy(table=test_table, convert=False)

print("\nFrequency table:")
print(ap.frequency_table)

ap.apriori(min_support=0.5)

for i in ap.association_tables.keys():
    print("\nAssociation table " + i)
    print(ap.association_tables[i])
```
## Output:

```
Test table
        items
0     1, 3, 4
1     2, 3, 5
2  1, 2, 3, 5
3        2, 5

Frequency table:
  item  frequency
0    1       0.50
1    2       0.75
2    3       0.75
3    4       0.25
4    5       0.75

Association table L1
  item  frequency
0    1       0.50
1    2       0.75
2    3       0.75
4    5       0.75

Association table L2
  itemset  frequency
1  (1, 3)       0.50
4  (2, 3)       0.50
6  (2, 5)       0.75
8  (3, 5)       0.50

Association table L3
     itemset  frequency
7  (2, 3, 5)        0.5

Test table:
   1  2  3  4  5
0  1  0  1  1  0
1  0  1  1  0  1
2  1  1  1  0  1
3  0  1  0  0  1

Frequency table:
  item  frequency
0    1       0.50
1    2       0.75
2    3       0.75
3    4       0.25
4    5       0.75

Association table L1
  item  frequency
0    1       0.50
1    2       0.75
2    3       0.75
4    5       0.75

Association table L2
  itemset  frequency
1  (1, 3)       0.50
4  (2, 3)       0.50
6  (2, 5)       0.75
8  (3, 5)       0.50

Association table L3
     itemset  frequency
7  (2, 3, 5)        0.5
```
ptscore 0.1.7
PassTheSecret core library containing back-end code for encryption and storage of secrets.
Requires Python: >=3.7

==================
PassTheSecret Core
==================

PassTheSecret Core is the heart of the different deployment methods for
PassTheSecret. The Core provides all encryption functionality and
interaction with the back-end data stores.

License
-------
PassTheSecret Core is free software: you can redistribute it and/or modify it 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.

PassTheSecret Core is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU General Public License for more details.

You should have received a copy of the GNU General Public License along with PassTheSecret Core.  If not, see <http://www.gnu.org/licenses/>.