semvec/pypi-packages huggingface dataset:
date-ranges 0.1.1
A DateRange type and related utility functions
Requires Python: >=3.11

# date-ranges

A `DateRange` type and related utility functions.

## Quickstart

Install from pip:

```shell
pip install date-ranges
```

Import and create an instance:
```python
from datetime import date

from date_ranges import DateRange

my_date_range = DateRange(start=date(2023, 5, 10), end=date(2023, 11, 3))
```

## Usage

```python
from datetime import date

from date_ranges import DateRange, MAX_DATE

# end date defaults to MAX_DATE
dr = DateRange(date(2023, 11, 1))

date(2023, 11, 22) in dr
# True

# An empty date (0 days in range):
empty = DateRange.empty()
bool(empty)
# False

# Iteration
for d in DateRange(date(2023, 1, 1), date(2023, 1, 3)):
    print(d)
# 2023-01-01
# 2023-01-02
# 2023-01-03

# Create from strings
dr = DateRange.from_string('20230101-20230103')
repr(dr)
# 'DateRange(start=datetime.date(2023, 1, 1), end=datetime.date(2023, 1, 3))'

# Create range for a single date
dr = DateRange.from_string('20231031')
repr(dr)
# 'DateRange(start=datetime.date(2023, 10, 31), end=datetime.date(2023, 10, 31))'

# Create range for given year-month
dr = DateRange.from_string('202305')
repr(dr)
# 'DateRange(start=datetime.date(2023, 5, 1), end=datetime.date(2023, 5, 31))'

# Create range for multiple full months
dr = DateRange.from_string('202305-202311')
repr(dr)
# 'DateRange(start=datetime.date(2023, 5, 1), end=datetime.date(2023, 11, 30))'

# Formatted print
dr = DateRange(date(2023, 1, 1), date(2023, 1, 3))
dr
# 20230101-20230103
print(f'{dr:H}')   # H for human
# 2023-01-01 to 2023-01-03

# Full months rendered appropriately
dr = DateRange.from_string('202305')
f'{dr:H}'  # H for "human-readable"
# 'May 2023'
f'{dr:C}'  # C for compact
# '202305'
dr
# '202305'
```


## Development

For those developing or maintaining the `date-ranges` package itself,
be sure to install it with the `[dev]` option to pull in packages
used when developing.

    pip install --editable .[dev]

When developing, this package uses `pre-commit`.  After the initial
clone of the repository, you will need to set up pre-commit with:

    # in the top level of the checked-out repository:
    pre-commit install

## Changelog

### 0.1.1 released 2025-06-27
* Fixed explicit exports

### 0.1.0 released 2025-06-26
* Add utility functions
  * weekday_after, weekday_before, first_weekday_of_month, last_weekday_of_month

### 0.0.2 released 2023-11-10
* Fixed typo

### 0.0.1 released 2023-11-03
* Initial Version
tensor-network-code 0.2.0
Tensor Network Code
Requires Python: >=3.12

# Tensor Network Notes

This repo contains my notes and code related to the course [Tensor Network](https://space.bilibili.com/401005433/lists/864780?type=season).

## Environment Setup

This is a [`uv`](https://github.com/astral-sh/uv) project. Setting up the environment is easy:

1. If you have not got `uv` installed, follow the [instructions](https://docs.astral.sh/uv/getting-started/installation/) to install it.
2. Run `uv sync` to create an environment and get dependencies, including development dependencies that you need to run the code in a notebook.

## Run Tools

We use `poe` to run tools. Available commands are:
* `lab`: run Jupyter Lab
* `sync`: sync the code in notebooks to the `tensor_network` package
* `format`: format the code in notebooks
* Checking: use ruff linter to check code 
    * `check_tensor_network`: check the exported code in `tensor_network` package
    * `check`: check code in `.`
    * `check_all`: check code in `.` and `tensor_network`
* `precommit`: run pre-commit hooks

Sample usage:
```shell
# if your shell detects venvs automatically, you can run poe directly
poe lab
# if your shell does not detect venvs automatically, you can run uv run poe directly
uv run poe lab
```

## Trained MPS Checkpoints

See [the repo](https://huggingface.co/mapleL/mnist_mps) on Huggingface.

## Contribution

Contributions are very welcome. Please file an issue or PR if you have any questions or suggestions.

A few points to note:

* The code should primarily live in notebooks, not Python scripts. We use `nbdev` to export useful code from notebooks to `tensor_network` package for reusability.
* Run `pre-commit install`, or `uv run pre-commit install` if your shell doesn't autodetect venv

## Acknowledgements

* Big thanks to Prof. Ran for the course
* Thanks to Gemini 2.0 and Claude Sonnet for transcribing a lot of equations
llama-index-graph-stores-agensgraph 0.1.0
llama-index graph stores agensgraph integration
Requires Python: <4.0,>=3.9
Keywords: llama-index, agensgraph, graph store, integration

# LlamaIndex Graph Store Integration: AgensGraph

This plugin provides support for AgensGraphStore integration in [llamaindex](https://www.llamaindex.ai/), for persisting graphs directly in [AgensGraph](https://github.com/skaiworldwide-oss/agensgraph). Additionally, we support the PropertyGraphIndex, which allows you to store and query property graphs in AgensGraph.

See the associated guides below:

- [Agens Graph Store](./examples/index_structs/knowledge_graph/AgensgraphDemo.ipynb)
- [Agens Property Graph Store](./examples//property_graph/property_graph_agensgraph.ipynb)


## Build

You can use the following commands to build the plugin from source.

```bash
pip install poetry
poetry install
poetry build
```

## Usage

```
pip install llama-index llama_index_graph_stores_agensgraph
```

### AgensGraphStore
```python
from llama_index.graph_stores.agensgraph import AgensGraphStore

conf = {
    "database": "",
    "user": "",
    "password": "",
    "host": "",
    "port": ,
}

graph = AgensGraphStore(graph_name="", conf=conf, create=True)
```

### AgensPropertyGraphStore
```python
from llama_index.graph_stores.agensgraph import AgensPropertyGraphStore

conf = {
    "database": "",
    "user": "",
    "password": "",
    "host": "",
    "port": ,
}

graph = AgensPropertyGraphStore(graph_name="", conf=conf, create=True)
```
langchain-graph-store-agensgraph 0.1.0
AgensGraph integration for LangChain graph stores
Requires Python: <4.0,>=3.9
Keywords: langchain, agensgraph, graph store, integration

# Langchain Graph Store Integration: AgensGraph

This plugin provides support for GraphStore integration in [langchain](https://www.langchain.com/), for persisting graphs directly in [AgensGraph](https://github.com/bitnine-oss/agensgraph).

See the associated guide below:

- [Agens Graph Store](./examples/agensgraph.ipynb)

## Build

You can use the following commands to build the plugin from source.

```bash
pip install poetry
poetry install
poetry build
```

## Usage

```
pip install langchain langchain_graph_store_agensgraph-0.1.0-py3-none-any.whl
```

```python
from langchain.graphs.agensgraph import AgensGraph

conf = {
    "database": "",
    "user": "",
    "password": "",
    "host": "",
    "port": ,
}

graph = AgensGraph(graph_name="", conf=conf)
```