semvec/pypi-packages huggingface dataset:
Pandora-Cloud-ZH 0.0.3
A package for Pandora-ChatGPT
Requires Python: >=3.7
Keywords: OpenAI ChatGPT ChatGPT-Plus

# Pandora-Cloud

![Python version](https://img.shields.io/badge/python-%3E%3D3.7-green)
[![Issues](https://img.shields.io/github/issues-raw/zhile-io/pandora-cloud)](https://github.com/zhile-io/pandora-cloud/issues)
[![Commits](https://img.shields.io/github/last-commit/zhile-io/pandora-cloud/master)](https://github.com/zhile-io/pandora-cloud/commits/master)
[![PyPi](https://img.shields.io/pypi/v/pandora-cloud.svg)](https://pypi.python.org/pypi/pandora-cloud)
[![Downloads](https://static.pepy.tech/badge/pandora-cloud)](https://pypi.python.org/pypi/pandora-cloud)

[![PyPi workflow](https://github.com/zhile-io/pandora-cloud/actions/workflows/python-publish.yml/badge.svg)](https://github.com/zhile-io/pandora-cloud/actions/workflows/python-publish.yml)

### A package for Pandora-ChatGPT
python-protobuf 0.0.2
Python Protobuf Package
Keywords: python,protobuf,proto,dict,encode,protocol


### Install
```
pip install python-protobuf
```

### Example
```py
from pyproto import Protobuf

print("Test the conversion between protobuf and bytes")
bs = bytes.fromhex("12001a0022020801") # A section of bytes data of protobuf
print("src:", bs.hex())
proto = ProtoBuf(bs)  # Convert bytes to protobuf objects
proto.dump()  # print proto object
bs2 = proto.toBuf()  # Convert the proto object to bytes
print(bs2 == bs, bs2.hex())

print("Mutual conversion between proto object and dict object")
# A complex dict object

d = {
    6: {
        1: {
            1: "eyJhbGciOiJIUzI1NiJ9.ODYxODAyNjMyNjcwMA.KGj7v_WjlntNODpPNe4fVbJA5sPhLjZbQidBLhcrGVM"
        },
        3: "5******7@*****.com",
        4: {1: 6},
        5: {1: 1},
        6: {2: "", 3: "", 4: {1: 1}},
    }
}

# An unassigned dict object template

d2 = {6: {1: {1: ""}, 3: "", 4: {1: 0}, 5: {1: 0}, 6: {2: "", 3: "", 4: {1: 0}}}}
print("src:", d)
pb = ProtoBuf(d)  # Convert dict object to protobuf object

pb.dump()

d3 = pb.toDict(d2)  # Convert protobuf object to dict object

print("d3 == d2 ->", d3 == d2, d3 is d2, d3)
print("d3 == d  ->", d3 == d, d3 is d)
```
uMAIA 0.2
Toolbox for the processing and analysis of MALDI-MSI data

# uMAIA
Toolbox for the processing and analysis of MALDI-MSI data


![alt text](figs/uMaiaLogo.png)


## Installation

To begin, create a new environment with 
`conda create --name uMAIA_env python==3.9`

activate the environment with
`conda activate uMAIA_env`

conda install jupyter with
`conda install jupyter`

install the required packages
`pip install -r requirements.txt`


If you are experiencing issues during the installation with caches files, you may need to remove them.
You can do so with the following commands:
`conda clean -a`
`python -m pip cache purge`
`rm ~/.cache/pip -rf`
`conda update --all`


### Gurobi
The molecule matching portion of the pipeline requires Gurobi. You will need to (1) install the Gurobi installer and (2) activate a license that is free for those are affiliated with an academic institution. Concerning (1), download the archive with 

Linux:
`wget https://packages.gurobi.com/9.1/gurobi9.1.2_linux64.tar.gz` to the directory you want to install it in.
or MacOS:
`https://packages.gurobi.com/9.1/gurobi9.1.2_macos_universal2.pkg`

Unpack the archive with

Linux:
`tar -xzvf gurobi9.1.2_linux64.tar.gz`
or on MacOS, simply double click the .pkg file and walk through the installation instructions

Activate the license with

Linux:
`gurobi912/linux64/bin/grbgetkey <licensekey>`
MacOS:
`grbgetkey <licensekey>`



### GPU usage restrictions
If you intend on applying uMAIA's normalisation algorithm on a GPU, please note that JAX will automatically use between 75 and 90 % of available GPU memory. To indicate that you would only like to allocate memory that is needed, you may export an environment variable after activating the environment with `export XLA_PYTHON_CLIENT_PREALLOCATE=false`