semvec/pypi-packages huggingface dataset:
climate-analyzer 0.1.0
Download & Analyze NOAA Climate Data
Requires Python: >=3.7

# climate_analyzer

climate_analyzer is a command line app that downloads NOAA weather station 
daily summary data and provides a gui to analyze year-to-year trends.
It also provides for searching a particular region for available NOAA
weather stations. climate_analyzer uses NOAA's Web Services API described
here: ([https://www.ncdc.noaa.gov/cdo-web/webservices]).

## Features
1. Search for available NOAA weather stations by state/county.
2. Download NOAA Climate Daily Summary Data by weather station. 
3. Store data in off-line database(s) organized by weather station.
4. Visualize with tkinter + matplotlib GUI  
5. Checks NOAA Web Services for updates to existing off-line database(s).

## Usage
climate_analyzer is a command line app that takes one and only one option.
If no option is provided, climate_analyzer checks for updates to existing
databases and then launches the visualization gui.  In order to access NOAA's
Web Services, an access token must be obtained and provided to climate_analyzer.

When installed with pip, climate_analyzer will add 'cda.exe' to the Python
Environment in the pythonXX\Scripts directory and a climate_analysis package to
pythonXX\Lib\site-packages.  cda.exe utilizes the climate_analysis package.
Config data is kept in cda.ini, also stored in pythonXX\Lib\site-packages.
### Options

#### --token [access-token]
Set the <access-token> used by climate_analyzer when connecting to NOAA's
Web Services.  The <access-token> is a 32-character alpha-numeric and can be
obtained from here here: https://www.ncdc.noaa.gov/cdo-web/token
This, and other configuration parameters are stored in
..\site-packages\climate_analyzer\cda.ini.

#### --findrgn [state]
Set the state & county region used by --find.  For each state, climate_analyzer
prompts with a list of possible counties (or parishes).  The state/county is
converted 5-digit fip_code and stored in cda.ini. See:
https://www.census.gov/library/reference/code-lists/ansi.html

#### --find [radius]
Display <STATION_ID> and other information for all weather stations within
the state & county set by -findrgn. 

#### --home [lat,long]
Set internal variable <HOME> used to calculate distance of weather station.

#### --station [alias]
Set <STATION_ALIAS> and <STATION_ID> used by --getcd.  The user is prompted
for the <STATION_ID>.  Any number of alias & id pairs may be set but a 
[alias, id] pair must be set inorder to download its climate data.

#### --getcd [alias]
Download all available daily summary climate data for [alias].  Daily summary
consists of [tmin, tmax, tavg, prcp, snow, snwd].  Data is stored in sqlite 
database @ %USERPROFILE%/AppData/ClimateData with a filename of [alias].  The
location used to store sqlite databases can be changed by editing cda.ini.
SQLITEWRAP 0.0.5
This is a simple wrapper class for sqlite
Requires Python: >=3.7

## Changes 

#### **0.0.2 (2023-03-06)**

* Initialization of the first commands without bug fixing,
* Push the release on pypi.

## Sqlite Wrapper 

To use python 3.7 is required.  
 * To initialise a new connection instance the class: `SqliteWrap(<connexionName>)` ,
* To create a new table call the function: `create_table("<tableName>" , listFields=[("<name>",<type>), ..]>)`,
* To add items to the different tables: `add_rows("<tableName>" , listFields=[("<name>",<value>), ..]>)`, 
* To find the maximum of a field in a table: `max_index("<tableName>" , <fields="<nameFieds>">)`, 
* To display the content of a table: `read_rows("<tableName>",<listFields=["<name>", ..]>)` , 
* To delete information from a table: `delete_rows("<tableName>" , "<condition=("<field>",<value>)>")`, 
* To empty a table: `reset_table("<tableName>")` ,
* To close the connexion with the db: `close_db()` .
keyway 1.0.4
A solution for persistent environment variables.
Requires Python: >=3.9

# Keyway
### Persistent environment variables!
A simple solution to prevent accidentally uploading your API keys to GitHub. Functions similarly to normal environment variables, except the keys and values persist through restarts.

```python 
pip install keyway 
```

Import with
```python
import keyway
kw = keyway.Keyway()
```
Set, access, and delete keys like a dictionary.
```python
kw["alpha"] = "an api key"
kw["bravo"] = "a setting"
kw["charlie"] = 42

kw["alpha"]
#>> "an api key"

del kw["alpha"]
```

Missing keys return None.
```python
kw["alpha"]
#>> None
```

Keys are not stored in memory.
```python
kw.keys()
#>> dict_keys([])
```

To retrieve a dictionary of all of a user's keys, use the "all" keyword. All keys are stored as text for simplicity. 
```python
kw[all]
#>> {'bravo': 'a variable', 'charlie': '42'}
```

To delete all of a user's keys, use the "all" keyword.
```python
del kw[all]
kw[all]
#>> {}
```

* By default, keys are stored in a sqlite database in the active environment folder
  * Never upload your virtual environment to github. 
  * Optionally set a custom path and/or database name like:
    * ``` python
      kw = keyway.Keyway(path = "your/custom/path", db_name = "custom_name")
      ```
  * You can also optionally set a user for the keyway instance: 
    * ```python
      kw = keyway.Keyway(user = "username")
      ```
  * The "all" keyword can be disabled with
    * ```python
      kw = keyway.Keyway(use_all = False)
      ```
  * Keys are encrypted using fernet.
    * This way, there is not a plaintext or easily accessible record of any of the keys.
    * Moving the database from its original location will cause the data to become unreadable. This is by design, so that even if someone has the database file, they are unable to access the keys without knowing the original path.
    * The cryptography library is the only external dependency.

* License: MIT
firconv 0.0.3
Python implementation of real-time convolution for auralization
Requires Python: >=3.6

# FIRconv
Python implementations of Finite Impulse Response (FIR) filters for real-time convolutions.

The algorithms are mainly *(but not strictly)* the ones described in **WEFERS, Frank. Partitioned convolution algorithms for real-time auralization. Logos Verlag Berlin GmbH, 2015**. found [here](http://publications.rwth-aachen.de/record/466561/files/466561.pdf?subformat=pdfa&version=1).


# Current algorithms
- Overlap-add (OLA);
- Overlap-save (OLS);
- Uniformily Partitioned Overlap-Save (UPOLS) (generalized version)

# Instalation
Setup FIRconv in your environment by running:
```
cd FIRconv
pip install -e .
```

___________________________________________________________________
Collaborations are more than welcome!
setuptools-cpp-cuda 0.1.7
The setuptools-cpp-cuda is a module that extends setuptools functionality for building hybrid C++ and CUDA extensions
Requires Python: >=3.6
Keywords: compilation,compile,cpp,c++,cross,cuda,ext,extension,setuptools,wrapper

# Setuptools C++ CUDA

***Warning:*** This project has been renamed to [pypi/setuptools-cuda-cpp](https://pypi.org/project/setuptools-cuda-cpp)
for visibility reasons in Pypi and migrated to GitHub for enhance community Issues feedback.

Please uninstall this module:

```pip uninstall setuptools-cpp-cuda```

and install the new package:

```pip install setuptools-cuda-cpp```

Sorry for the inconveniences.