semvec/pypi-packages huggingface dataset:
opentelemetry-instrument-anthropic 0.0.2
Instruments Anthropic client for OpenTelemetry
Requires Python: >=3.7.1,<4.0

# opentelemetry-instrument-anthropic-py

Autoinstrumentation for Anthropic's client library.

Project site: https://github.com/fxchen/opentelemetry-instrument-anthropic-py

# Usage

**Installaion**

```
poetry add opentelemetry-instrument-anthropic
poetry run opentelemetry-bootstrap -a install
poetry run opentelemetry-instrument python your_app.py
```

**Using auto instrumentation agent**

```
poetry add opentelemetry-instrument-anthropic
poetry run opentelemetry-bootstrap -a install
poetry run opentelemetry-instrument python your_app.py
```

**Manually**

```
from anthropic import Anthropic, HUMAN_PROMPT, AI_PROMPT
from opentelemetry.instrumentation.anthropic import AnthropicInstrumentor

AnthropicInstrumentor().instrument()

client = Anthropic()
client.completions.create(
    prompt=f"{HUMAN_PROMPT}\nHello world\n{AI_PROMPT}",
    )
```

## 🔗 Example [chat.py](./example)

In this folder there's a readme on how to:

- Execute a chat completion against Anthropic
- Send traces to your console or your OLTP provider!

# Acknowledgments

Thank you very much to [Phillip Carter](https://github.com/cartermp) and instrumentation by the OpenTelemetry Community! This instrumentation was heavily inspired by conversation and this [OpenAI library](https://github.com/cartermp/opentelemetry-instrument-openai-py/tree/main).
gooderror 0.0.3
GoodError is a Python library that enhances exception handling, including integration with GPT-3 for additional context.
Keywords: exception handling,GPT-3,Python library


![Alt Text](https://i.ibb.co/LdxyRp5/image-2023-09-13-23-36-08.png)

# GoodError Documentation

The GoodError library provides an improved exception handling mechanism in Python. It allows you to enhance the output of exceptions, including sending them to GPT-3 for additional context.

## Installation

To install GoodError, use pip:

```bash
pip install gooderror
```


## Example

Here's an example of how to use GoodError:

```python
from gooderror import GoodError

GoodError()

x = "SADfsadf" + 5

# GoodError will enhance the exception output and will add COLORS!
# OUTPUT:
#<module>, TypeError(Can only concatenate str (not "int") to str)
#main, Line 82 -> m = "SADfsadf" + 5

```

## Class: GoodError

The GoodError class provides the following attributes and methods:

### Attributes

- `gpt_key` (str): Your GPT API key.
- `use_colors` (bool): Whether to use colors in the output.


## GPT3.5 Usage

Import the GoodError class and initialize it with your GPT API key.

```python
from gooderror import GoodError

# Initialize GoodError with your GPT API key
GoodError(gpt_key="YOUR_GPT_API_KEY")

result = 0/0

# OUTPUT:
#<module>, ZeroDivisionError(Division by zero)
#main, Line 80 -> result = 0/0

#Waiting for GPT to respond...

#The error "ZeroDivisionError" is raised when you try to divide a number by zero in Python. Division by zero is not defined in mathematics, hence this error is generated to indicate an invalid operation.
#To fix this error, you need to make sure you do not divide a number by zero. If the denominator can potentially be zero in your code, you can add a check to handle this scenario using an if condition. For example:

#try:
#    result = 0/0
#except ZeroDivisionError:
#    print("Cannot divide by zero")

#In this case, if the division operation encounters a ZeroDivisionError, it will be caught by the except block, which will then print the error message.

```
lilyanncabinets 2.0.2
Package for use by Lily Ann Cabinets
Requires Python: >=3.6

Current Version: 2.0.2

Patch Notes: 

===========================================================================

[2.0.2]: Fixed issue with returning data 

===========================================================================

[2.0.1]: Added new module for scanning excel sheets with the command "search_excel_file(sheet_name, keyword, row_offset, column_offset, search_in_column, search_in_row). 

sheet_name: Name of the sheet within the file you would like to search through

keyword: Keywords you would like to search for, format ["keyword1", "keyword2"]

row_offset: Number of rows offset the target would you like to read from

column_offset: Number of column offset the target would you like to read from

search_in_column: Defaults to "None" lets you pick a specific column to read from, ex. "A", "B", or "C"

search_in_row: Defaults to "None" lets you pick a specific column to read from, ex. 1, 2, or 3


===========================================================================

[1.5.3]: Fixed patchnotes formating 

===========================================================================

[1.5.2]: Fixed issue where writeToSheet() would just create a new sheet and delete the previous data

===========================================================================

[1.5.1]: Creating a new "build" added an auto spreadsheet creating and updating function for use with both robot and manual data tracking, 

- added createNewSheet(): to create the sheet (names the sheet the current date)

- added writeToSheet(): Use to write data to the created sheet although running "createNewSheet()" is not required as the function will create one if a sheet under the current date cannot be located. Format for the function is writeToSheet(Row, Column, data)

===========================================================================

[0.5.2]: "Added equations for "limelight" functions"

===========================================================================

[0.5.1]: "Started Unstable build 0.5"

===========================================================================

reminder to py -m build
and py -m twine upload --repository pypi dist/*
cronpar 1.0.0
A Cron Expression Parser
Requires Python: >=3.8.1,<3.12

# Cronpar - a tool for parsing cron expression

### Requirements

To run this program, you need setup you local environment as following

- A Python Environment with Python 3.8+ and pip (reckon use pyenv and create a virtualenv)
- Poetry (Dependency management)
- make (a dev tool)

### Setup

Before you can run everything, please make sure you have setup a Python environment

if you are using make, you can setup the dev environmane by simply using

```shell
make init
```

this will install poetry and install all dependencies. If you don't want to use make, alternatively, just run the 
following commands

```shell
pip install poetry
poetry install
```

To prepare/build the cli tool for you to play with, run

```shell
make build
```

or, just run

```shell
pipx install .
```

Now, you should be able to run the program, for example

```shell
cronpar explain "*/15 0 1,15 * 1-5 /usr/bin/find"
```

### Yesting & Contribution

There are some test cases generated during development, to run the tests, you can either run

```shell
make test
```

pr 

```shell
poetry run pytest tests/ --cov --cov-fail-under 89
```

Please make sure you have tested all the changes you made properly, and all tests passed before you push

Also, it will be good to keep the code formatted, you can check this by running

```shell
make format
make lint
```

or 

```shell
poetry run black --preview --line-length 120 cronpar
poetry run isort --line-length 120 cronpar
poetry run flakehell lint .
```

### Notice

Please Note this program is still in development, it may be not very stable,
but we will fix and add more features in the coming future.
impt 0.1.2
Auto-diff Estimator of Lensing Perturbations
Requires Python: >=3.8

# imPT
----
[![tests](https://github.com/mr-superonion/lensPT/actions/workflows/tests.yml/badge.svg)](https://github.com/mr-superonion/lensPT/actions/workflows/tests.yml)
[![pypi](https://github.com/mr-superonion/imPT/actions/workflows/pypi.yml/badge.svg)](https://github.com/mr-superonion/imPT/actions/workflows/pypi.yml)
[![Documentation Status](https://readthedocs.org/projects/impt/badge/?version=latest)](https://impt.readthedocs.io/en/latest/?badge=latest)
[![License: GPL v3](https://img.shields.io/badge/License-GPLv3-blue.svg)](https://www.gnu.org/licenses/gpl-3.0)
[![Code style: black](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/psf/black)

Fast estimator for Lensing Perturbation (`impt`) on astronomical images using
the auto differentiating function of jax. The code need to be combined with
estimator (e.g. [FPFS](https://github.com/mr-superonion/FPFS)) compressing image
to catalog. It is a simple code to infer shear from the catalog with correction for noise bias

----

## Installation

For stable (old) version, which have not been updated:
```shell
pip install impt
```

For developers, please clone the repository:
```shell
git clone https://github.com/mr-superonion/imPT.git
pip install -e . --user
```

## Summary
For the first version of `imPT`, we implement `FPFS` and use `imPT` to auto
differentiate `FPFS`.