semvec/pypi-packages huggingface dataset:
zinfoexport 0.2.8
Package to extract data from z-info (historian)
Requires Python: >=3.6

This package contains a client to make a data request from the z-info API.
--------------------------------------------------------------------------------------------------

To create a client you need a json file with the following parameters:

username: str\
    This is your z-info username\
password: str\
    This is your z-info password\
file_name: str\
    Name for your export file\
file_format: str\
    Can either be ".csv" or ".feather"\
input_file: str \
    Name for your input csv. It is mandatory to have a header row with these three values: tagnr, name, wbm\
startdate: str\
    Start date in YYYY-MM-DD format\
enddate: str\
    End date in YYYY-MM-DD format\
periode: str\
    time for which values will be aggregated. can be anything between 1-9 s/m/h (for example: 5m = 5 minutes)\
interval: str\
    To avoid making requests for too long intervals this spilts the dates up in intervals. Default is 10 which works fine.\
waterschapnummer: str\
    Waterschap specific number.\
spcid: str\
    Waterschap specific spcid.\

You then need to specify the file name to create a client.\
With this client you can use the method run to make a request based on your parameters.\
If you create a client without a parameters file, it will generate a template which you can then fill in and supply using the function set_parameters("name of your parameter file")
hades-hash 0.1.8
Hades is a complex hashing algorithm written in Python which generates a 128-bit hex digest.

![Python application](https://github.com/thisisjustinm/hades/workflows/Python%20application/badge.svg?branch=master)
![Upload Python Package](https://github.com/thisisjustinm/hades/workflows/Upload%20Python%20Package/badge.svg)
![CodeQL](https://github.com/thisisjustinm/hades/workflows/CodeQL/badge.svg)

## Hades
>Hades is a complex hashing algorithm written in Python.
Hades makes use of the following algorithms to generate a **128-bit hex digest**.

*   Ares (Simple Hashing Algorithm)
*   Zeus (Simple Encoding Algorithm)

## Usage
Install using ```pip```
```bash
pip install hades-hash
```
Once installed, it can be used 



<details><summary>1. for generating hashes</summary>
 
 ```python
from hades import hades
h = hades.Hades('seed') # initialise with a seed phrase

#Create single hash
print(h.single_hash())
#d54b8d9081353396a28f867820750118

#Create hash of variable bit size
print(h.get_var_bit_hash(256))
#7a55635276c86d968e6b04d428d8477216125bb48273c12fe3d86acd95db5680

#Create a list of n random hashes 
print(h.n_random_hashes(3))
#['8eb4486e3dd693187f812067d865a279','386e90fddd413236d210cae90f02247d','4a4363eb636d8af538c10f3b5d235509']
```
</details>
<details><summary>2. performing file checksum</summary>

```python
from hades import hades
h = hades.Hades('path/to/file/some_file.txt') #file path

#Get file checksum
print(h.get_file_checksum())
#06921338e06e3677f463db08ac208758

#Compare file checksum
print(h.check_file_checksum('06921338e06e3677f463db08ac208758'))
#True
```
</details>
<details><summary>3. generating a visual checksum</summary>

```python
from hades import hades
h = hades.Hades('seed') # initialise with a seed phrase

#Create a visual checksum
print(h.generate_visual_checksum('svg')) #[options:svg,png,jpg]
#38a8131be0258a14b4e55704b56ec497.svg saved to C:/
```
```38a8131be0258a14b4e55704b56ec497.svg``` will look like 

![visual hash](/assets/38a8131be0258a14b4e55704b56ec497.svg)
</details>


## Extra

So far, it is collision resistant, and has pretty fair avalanche effect.


|  ```string```  | ```hash```  |
| --- | ----------- |
| ```I am Hades, a hashing algorithm.``` | ```2ca53472bae9fa0b680d2184965ce042``` |
| ```I am hades, a hashing algorithm.``` | ```f557bf77c1df4649e1ac03f7626b08cd``` |
|  ```I am Hades, a hashing algorithm``` | ```9d3fe1e62c034cdbdafbf8b0358f8f7b``` |
|  ```I am Hades. a hashing algorithm``` | ```64c50e25ad52bc20071cea4181bc39a7``` |
|  ```i am Hades, a hashing algorithm``` | ```9eff43c45d63f1ca3925b2e4c18f6afe``` |
|  ```I am Hades,a hashing algorithm.``` | ```74aeb725355fccad4f8154f147d41cf1``` |
|  ```I'm Hades, a hashing algorithm```  | ```6415c4512427cca312afe784e73883c0``` |
|  ```I am Iades, a hashing algorithm``` | ```a6534aafb71acfa3230adc18d584bd1c``` |
Dicom-To-CNN 0.0.1
Python Library to handle Input / Output conversion in Dicom <=> Convolutional Neural Network
Requires Python: >=3.6

# library-DICOM

Features : 
- Description of Series content in a huge dataset of DICOM (output JSON descriptor for each series containings main DICOM tags).
- Conversion Dicom to Nifti
- PET : Conversion Bqml/Counts to SUV and SUL

Roadmap : 
- Read RTSS to generate Mask
- Generate RTSS from Mask
- PT / CT fusion in 4D array np array

#Maintainer  : Salim Kanoun
#Contributors : Thomas Trouillard, Wendy Revailler



To refactor : 
- conversion of a nifti mask to a ROI in a DICOM RTSTRUCT
- ROI integration to an existing RTSTRUCT
- generation empty RTSTRUCT from PET,CT or similar set of DICOM images

**TODO :** 
- conversion DICOM RTSTRUCT to mask in nifti format