semvec/pypi-packages huggingface dataset:
pytest-pointers 0.3.1
Pytest plugin to define functions you test with special marks for better navigation and reports
Requires Python: >=3.7,<4.0
Keywords: plugin,pytest

## Pytest Plugin to Show Unit Coverage

**Code** coverage tools like
[coverage.py](https://coverage.readthedocs.io/en/7.0.1/) show you the
instrumented code coverage of your tests, however it won't tell you if you've
written specific unit tests for each of your code's **units** (here unit meaning
function).

This package implements a mechanism for measuring and reporting unit coverage.
Instead of instrumenting your code you will need to mark tests with a
**pointer** to the unit that it is covering.

For example if you have in your code this module `mypackage/widget.py`:

``` python
def foo(in):
    return in * 3
```

then in your test suite you would write a unit test for this function and mark it as relating to that unit, e.g. in `tests/test_widget.py`:

``` python

from mypackage.widget import foo

@pytest.mark.pointer(foo)
def test_foo():
    assert foo(3) == 9
```

This package works by collecting all of the pointed-to units during test
execution and persists these to the pytest cache (typically somewhere under
`.pytest_cache`). Then in subsequent runs you need only report the results.

### Invocation

This package adds a couple new options to the `pytest` CLI:

`--pointers-collect=STR` (default `src`)

This explicitly indicates to collect unit coverage results. If not specified,
but `--pointers-report` is given results will be collected using the default.

`--pointers-report` (default `False`)

When this flag is given a textual report will be given at the end of the test
run. Note that even if this is not given the coverage checks will still be run.

`--pointers-func-min-pass=INT` (default `2`)

This flag controls the number of unit test pointer marks are needed to get a
"passing" unit. In the report units with 0 pointers are shown as red, passing
numbers are green, and anything in between is blue.

`--pointers-fail-under=FLOAT` (default `0.0`)

This flag controls the percentage of passing units are needed for the entire
coverage check to pass. The percentage is always displayed even without
`--pointers-report`. If this test is failed then the test process exits with
code 1, which is useful for things like CI.

#### Example

Here is an example with source code under the `src` folder, requiring 1 pointer
test per collected unit in the code, for all functions.

```
pytest --pointers-report --pointers-collect=src --pointers-func-min-pass=1 --pointers-fail-under=100 tests
```

![](https://jaklimoff-misc.s3.eu-central-1.amazonaws.com/pytest-pointers/example_output.jpg)

### Installation

``` shell
pip install pytest_pointers
```
smiles-featurizers 1.0.8
A python library for extracting molecular SMILES embeddings from language models pre-trained with various objectives and/or architectures.
Requires Python: >=3.8

# SMILES Featurizers

Extract SMILES embeddings from language models pre-trained with various objectives architectures.

## Getting Started

```bash
pip install smiles-featurizers
```

## Model List

Our released models are listed as following. You can import these models by using the `smiles-featurizers` package or using [HuggingFace's Transformers](https://github.com/huggingface/transformers).
| Model | Type |
|:-------------------------------|:--------:|
| [shahrukhx01/smole-bert](https://huggingface.co/shahrukhx01/smole-bert) | Bert|
| [shahrukhx01/smole-bert-mtr](https://huggingface.co/shahrukhx01/smole-bert-mtr) | Bert|
| [shahrukhx01/smole-bart](https://huggingface.co/shahrukhx01/smole-bart) | Bart|
| [shahrukhx01/muv2x-simcse-smole-bart](https://huggingface.co/shahrukhx01/muv2x-simcse-smole-bert) | Simcse|
| [shahrukhx01/siamese-smole-bert-muv-1x](https://huggingface.co/shahrukhx01/siamese-smole-bert-muv-1x) | SentenceTransformer|

## Use SMILES Featurizers

### Bert Featurizer

```python
from smiles_featurizers import BertFeaturizer
import torch

## set device
use_gpu = True if torch.cuda.is_available() else False

featurizer = BertFeaturizer("shahrukhx01/smole-bert", use_gpu=use_gpu)
embeddings = featurizer.embed(["CCC(C)(C)Br"])
```

### Bart (Encoder) Featurizer

```python
from smiles_featurizers import BartFeaturizer

featurizer = BartFeaturizer("shahrukhx01/smole-bart")
embeddings = featurizer.embed(["CCC(C)(C)Br"], embedder="encoder")
```

### Bart (Decoder) Featurizer

```python
from smiles_featurizers import BartFeaturizer

featurizer = BartFeaturizer("shahrukhx01/smole-bart")
embeddings = featurizer.embed(["CCC(C)(C)Br"], embedder="decoder")
```

### SimCSE Featurizer

```python
from smiles_featurizers import SimcseFeaturizer
import torch

## set device
device = "cuda" if torch.cuda.is_available() else "cpu"

featurizer = SimcseFeaturizer("shahrukhx01/muv2x-simcse-smole-bert", device=device)
embeddings = featurizer.embed(["CCC(C)(C)Br"])
```

### SentenceTransformer Featurizer

```python
from smiles_featurizers import SentenceTransformersFeaturizer
import torch

## set device
device = "cuda" if torch.cuda.is_available() else "cpu"

featurizer = SentenceTransformersFeaturizer("shahrukhx01/siamese-smole-bert-muv-1x", device=device)
embeddings = featurizer.embed(["CCC(C)(C)Br"])
```
csv2img 1.0.0
Package to save CSV as PNG
Requires Python: >=3.4

# csv2img

This is a project that save CSV file as PNG images.

## Installation 

Run the following to install: 

```python 
pip install csv2img
```


### Pre-requisite
To use csv2img, make sure that you have installed the package of csv2pdf, fitz and PyMuPDF, since this package is a combination of csv2pdf and fitz.

```python 
pip install csv2pdf
pip install fitz #please install fitz first, else it may crush with PyMuPDF
pip install PyMuPDF
```
#### Usage
``` python 
from csv2img import saveas
saveas(csv_file)
```

##### Example
``` python 
from csv2img import saveas
saveas("data.csv")
```

###### Error Handling
If the occured error is related to the packages of fitz and PyMuPDF, please uninstall these packages and install them again. 

```python 
pip uninstall fitz
pip uninstall PyMuPDF
pip install fitz #please install fitz first, else it may crush with PyMuPDF
pip install PyMuPDF
```
localcloud 0.0.1
[Experimental] CLI tool allowing to imitate cloud components locally for testing their integration
Requires Python: >=3.8

`localcloud` - CLI tool allowing to imitate cloud components locally for testing their integration.

## Usage
Clone code repositories into working directory and run `localcloud`, which will read 
Terraform configuration from all of the repositories and will locally create their
imitations (using docker containers and mocks). Now you should have all of the resources
working locally and you should be able to run integration tests on them, e.g. `make run_integration_tests`.

## Roadmap
- [ ] Add Lmabda support
- [ ] Add SQS support
- [ ] Add EventBridge support
- [ ] Add generic resource mock, which allows to define interface wrappings.

## Development
`make venv` - will create virtual env with dev dependencies.
`make check` - will run Flake8, MyPy and PyTest checks.

## Related projects
This tool was inspired by:
- https://localstack.io