semvec/pypi-packages huggingface dataset:
edam-ontology.py 1.25.1
Versioned, Python packaged EDAM ontology (http://edamontology.org/) data.
License: MIT
Keywords: EDAM


edam-ontology.py
-------------------

EDAM is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License (CC BY-SA 4.0) and
can be found at http://edamontology.org/.

EDAM is described in the following article. If you use, or refer to EDAM or its part, please cite:

    Ison, J., Kalaš, M., Jonassen, I., Bolser, D., Uludag, M., McWilliam, H., Malone, J., Lopez, R., Pettifer, S. and Rice, P. (2013). EDAM: an ontology of bioinformatics operations, types of data and identifiers, topics and formats. Bioinformatics, 29(10): 1325-1332. DOI: 10.1093/bioinformatics/btt113 PMID: 23479348 The article is freely available without restrictions (Open access)

This project is just a Python packaged version of that data - initially developed to avoid non-Python dependencies
within Galaxy packages that need to leverage the ontology.




History
-------

.. to_doc

---------------------
1.25.1 (2021-08-03)
---------------------

* Attempt to fix automated publishing to PyPI.

---------------------
1.25.0 (2021-08-02)
---------------------

* Initial project creation including the tabular version of EDAM 1.25 (https://zenodo.org/record/3899895#.YP7TqBNKh6o).
tpd-analyse 0.2.0
A python module of determining adsorption energies from temperature programmed desorption curves
License: MIT
Requires Python: >=3.6

TPD analysis for first order desorption process
-----------------------------------------------

## Details

Calculate the adsorption energy, configurational entropy and adsorbate-adsorbate interaction parameter for first order desorption from TPD curves. 

Equations used to fit are described in the manuscipt: How to Extract Adsorption Energies, Adsorbate-adsorbate Interaction Parameters, and Saturation Coverages from Temperature Programmed Desorption Experiments ([link](https://chemrxiv.org/engage/chemrxiv/article-details/60c75888567dfe0feeec6887))

## Installation

The package can be installed via

```
pip install tpd-analyse
```

## Usage

All inputs must be passed to the central class `PlotTPD`. An example of some possible inputs are shown below. 

```
    TPDClass = PlotTPD(exp_data=files,
                        order=order,
                        thermo_ads=vibration_energies_ads,
                        thermo_gas=vibration_energies_gas,
                        plot_temperature=np.linspace(100, 500, 50), 
                        T_switch=T_switch_211,
                        T_max=T_max_211,
                        T_rate_min=T_rate_min_211,
                        beta=beta_211)
```

1. `exp_data`: list of filenames. Each filename must be of the format `exposure_<some_value>.csv` where some_value can be the exposure in any units. 
2. `order`: currently only first order desorption reactions are implemented, so `order=1`
3. `thermo_ads`: Here the `ase` class `HarmonicThermo` can be passed with the required inputs for the adsorbate(s) of interest. More information about the class can be found [here](https://wiki.fysik.dtu.dk/ase/ase/thermochemistry/thermochemistry.html).
4.  `thermo_gas`: Similar to the `thermo_ads`, only for gas molecules. One option is to use `IdealGasThermo`
5.  `plot_temperature`: A temperature range that the equibirium coverage can be determined in. An example range would be `np.linspace(100, 500)
6.  `T_switch`: Is there are more than one processes occuring in one TPD plot, supply a list of temperatures to switch from one to another in K.
7.  `T_max`: Maximum temperature to consider for *all* the TPDs. 
8.  `T_rate_min`: Minimum temperature at which the baseline correction can be done. 
9.  `beta`: Heating rate in K/s
10.  `correct_background` (optional): bool to decide if the background is corrected
11.  `p` (optional): Pressure in the case of *equilibirum*, only useful if equilbirum coverages are needed
12.  `initial_guess_theta` (optional): Initial guess for the coverage in the Newton root solver
13.  `guess_b`(optional): Guess for the ads-ads interaction. 
14.  `calculate_eq_coverage` (oprtional): Decide is the equilibrium coverage is computed.
null-bot-api 1.0.0
Null Bot API wrapper
License: MIT

# NULL BOT API wrapper



![PyPI](https://img.shields.io/pypi/v/null-bot-api)
![PyPI - Python Version](https://img.shields.io/pypi/pyversions/null-bot-api)
![GitHub](https://img.shields.io/github/license/lordralinc/null_bot_api)

## Установка 
```shell
pip install -U https://github.com/lordralinc/null_bot_api/archive/master.zip
```

или 

```shell
pip install null_bot_api
```

## Использование

```python
from null_bot_api import NullBotApi

api = NullBotApi()

stickers = api.users.get_stickers()

custom = api.make_request(
    "section.method", 
    data=dict(param1="foo", param2="bar"), 
    dataclass=dict
)
```

```python
# Асинхронное использование
from null_bot_api import NullBotApi

api = NullBotApi()

stickers = await api.users.get_stickers_async()

custom = api.make_request_async(
    "section.method", 
    data=dict(param1="foo", param2="bar"), 
    dataclass=dict
)
```

## Методы
Методы предоставлены на [странице](https://vk.com/@null_bot-api).
Все методы следует преводить в snake_case.
ExGrads 0.1.12
calculate example-wise gradient
License: MIT

this repository is still under construction (2021/07/21)


ExGrads
===
This repository provides a hook script: calculating Example-wise Gradients efficiently.


Note
---
This script use [the work](https://github.com/cybertronai/autograd-hacks) as an important reference.\
I think it is the great first step to handle per-example gradients efficiently.\
I'd like to express my respect for the step.


Features of This Script
----
+ Calculate example-wise gradient efficiently\
	There is no method calculating Hessian in contrast to [the referenced work](https://github.com/cybertronai/autograd-hacks).
+ Handle general modules\
	Including Linear, Conv2d, BatchNorm2d, and BatchNorm1d. More modules will be added soon.
+ How to use this script in practice
	1. [Fast and Exact calculating $`\text{tr}[\bold{H}]`$](https://gitlab.com/takuo-h/fast-exact-trh)


How to Use
----
```python
import torch
import exgrads as ExGrads

batch,dim,label = 5,3,2
x = torch.randn(batch,dim)                                  #: inputs
y = torch.randint(low=0,high=label-1,size=(batch,))         #: outputs
model   = torch.nn.Sequential(torch.nn.Linear(dim, label))  #: PyTorch model
loss_fn = torch.nn.functional.cross_entropy                 #: loss function

ExGrads.hooks.register(model)
model.zero_grad()
loss_fn(model(x), y).backward()
ExGrads.hooks.compute_grad1(model)

# param.grad:     gradient averaged over the batch
# param.grad1[i]: gradient of i-th example
for param in model.parameters():
	assert(torch.allclose(param.grad1.sum(dim=0), param.grad))

ExGrads.hooks.deregister(model)
```