semvec/pypi-packages huggingface dataset:
SpaGCN 1.2.7
SpaGCN: Integrating gene expression and histology to identify spatial domains and spatially variable genes using graph convolutional networks
Requires Python: >=3.6

# SpaGCN

## SpaGCN: Integrating gene expression and histology to identify spatial domains and spatially variable genes using graph convolutional networks

### Jian Hu,*, Xiangjie Li, Kyle Coleman, Amelia Schroeder, Nan Ma, David J. Irwin, Edward B. Lee, Russell T. Shinohara, Mingyao Li,*

SpaGCN is a graph convolutional network to integrate gene expression and histology to identify spatial domains and spatially variable genes. To jointly model all spots in a tissue slide, SpaGCN integrates information from gene expression, spatial locations and histological pixel intensities across spots into an undirected weighted graph. Each vertex in the graph contains gene expression information of a spot and the edge weight between two vertices quantifies their expression similarity that is driven by spatial dependency of their coordinates and the corresponding histology. To aggregate gene expression of each spot from its neighboring spots, SpaGCN utilizes a convolutional layer based on edge weights specified by the graph. The aggregated gene expression is then fed into a deep embedding clustering algorithm to cluster the spots into different spatial domains. After spatial domains are identified, genes that are enriched in each spatial domain can be detected by differential expression analysis between domains. SpaGCN is applicable to both in-situ transcriptomics with single-cell resolution (seqFISH, seqFISH+, MERFISH, STARmap, and FISSEQ) and spatial barcoding based transcriptomics (Spatial Transcriptomics , SLIDE-seq, SLIDE-seqV2, HDST, 10x Visium, DBiT-seq, Stero-seq, and PIXEL-seq) data. 
For more info, please go to: 
https://github.com/jianhuupenn/SpaGCN
tinote 1.2.1
A command-line note-taking tool
Requires Python: >=3.6

![Logo](https://raw.githubusercontent.com/aPeter1/tinote/main/assets/color-logo-no-background.png)

## No Thought Left Behind.

#### Huh, that's a pretty good idea. I should write that down. 

###### [proceeds to lose the paper]

#### Wow, I should write down that task for later in Notepad!

###### [proceeds not to save the file before computer restarts that night]

#### Ugh.

Tinote is light-weight command-line note-taking tool, designed for quick and easy note management. Keep track of your thoughts, ideas, or reminders directly from the terminal.

###### (made entirely for myself, but feel free to use it and contribute!)

## Installation

Install Tinote using pip:

```bash
pip install tinote
```

## Usage

Create a new note (if no category is provided, the last category will be used):

```bash
ti add "a note to remember" <optional category> <optional importance>
```

List all notes (verbose list will include importance and timestamp):

```bash
ti list
```

```output
IDEAS---------------------------------------------------------------------------
[ ] 55 A dating app, but for hamster-owners ONLY
[✔] 58 A sushi company that sell BEEF sushi! :O

VAN-STUFF-----------------------------------------------------------------------
[✔] 55 Fix the latch for the fridge
[ ] 58 Install Solar
    [ ] 60 Install roof-rack, hopefully easy
        * How hard could it be
        * Hahaha
    [ ] 61 Attach panels
    [ ] 62 Run wiring
```
As you can see, using * in a message (no newline necessary) will cause that to be indented on the next line down.

List notes with filters (category or importance or marked) or higher verbosity:

```bash
ti list -c <category> -i <importance> -m -u -v
```

Add a sub-note to a note (can be as recursive as you can dream)

```bash
ti sub <parent_id> "Text of your sub-note" -i <importance>
```

Mark a note as checked/unchecked (-u indicates to unmark):

```bash
ti mark <note_id> -u 
```

Delete a note (deletes all children notes):

```bash
ti delete <note_id>
```

Clear all notes or notes within a specified category:

```bash
ti clear
ti clear -c <category>
```

## Features

- Create and manage notes with optional categories and importance levels
- List notes, with optional filters for categories and importance
- Easily mark notes as checked/unchecked
- Delete notes using their ID
- Clear all notes or notes within a specified category
- Automatically saves notes to a local JSON file for persistence

## License

MIT License
Dickens 2.1.1
Additional decorators implementing the descriptor interface

=======
Dickens
=======

Additional Python decorators implementing the descriptor interface.

Use cases
=========

Like the built-in decorator, ``property``, these classes are initialized by and wrap a function, generally within the context of a class, in order to modify its behavior.

cached property
---------------

This decorator functions much like a read-only ``property``, with the difference that, upon access, it records its result in the instance's object data dictionary, which reference takes precedence in look-ups, thereby replacing itself for that object::

    from descriptors import cachedproperty

    @cachedproperty
    def circumference(self):
        return 2 * math.pi * self.radius

class property
--------------

A read-only ``property`` for class methods::

    from descriptors import classproperty

    @classproperty
    def badpi(cls):
        return 22 / 7

cached class property
---------------------

A class ``property``, which caches its result in the data dictionary of the class from which it was invoked, (under another name, so as not to interfere with inheritance of the property)::

    from descriptors import cachedclassproperty

    @cachedclassproperty
    def badpi(cls):
        return 22 / 7

class-only method
-----------------

A class method that **cannot** be accessed as an instance method::

    from descriptors import classonlymethod

    @classonlymethod
    def circumference(cls, radius):
        return 2 * cls.pi * radius

The class-only method *may* be overshadowed by instance data set under the same name.

Otherwise, instance access raises ``AttributeError`` (and such that it is forwarded to and *may* be handled by the instance's ``__getattr__``).

Installation
============

Dickens is a Python distribution, which may be installed via ``easy_install`` or ``pip``, *e.g.*::

    pip install Dickens

...or, from source::

    python setup.py install
cloudlab-client 0.1.83
Client for the cloudlab academic cloud environment.
Requires Python: >=3.8,<4.0

# Cloudlab Client

This package is a client for the Cloudlab service. Cloudlab is a cloud for
academic institutions. Because I could not (yet) find any working API for
Cloudlab, the current client relies on username / password authentication and
web scraping.


## Usage


```python
# Create new client and login
username = os.environ.get("CLOUDLAB_USERNAME")
password = os.environ.get("CLOUDLAB_PASSWORD")
cloudlab_client = CloudlabClient()
cloudlab_client.login(username, password)

# List experiments
experiments = cloudlab_client.experiment_list()
print(experiments)

# List an experiment's nodes
nodes = cloudlab_client.experiment_list_nodes("my-experiment")
print(nodes)

# Request an extension (e.g., for 6 days). Reason must be at least 120 characters.
reason = ("Important experiment needed for research, conducted under advisor"
          " <fill_your_advisor>. Particular machines are needed because"
          " <fill_your_reasons>.")
cloudlab_client.experiment_extend("my-experiment", reason, hours=6*24)
```
profiler-wrapper 1.0.1
Wrapper script for easier use of the built-in profiler
Requires Python: >=3.3

# Python3 - Profiler Wrapper

This package provides a wrapper function for easier usage of the built-in profiler.

It only uses builtin modules.

## Install

```bash
python3 -m pip install profiler-wrapper
```

See: [PyPI](https://pypi.org/project/profiler_wrapper/)

## Usage

```python3
from profiler_wrapper import profile

def function_to_profile(switch: bool, data: dict, msg: str):
    ...
    return {'data': 'test'}


profile(
    target=function_to_profile,
    args=[True],
    kwargs={
        'data': {'random': 'test'},
        'msg': 'Yesterday is gone.'
    },
    lines=3,
)

# (
#   {'data': 'test'},
#
#   "270011472 function calls (270011215 primitive calls) in 181.830 seconds
#    Ordered by: internal time
#
#    ncalls  tottime  percall  cumtime  percall filename:lineno(function)
#         1    5.000    5.000    8.000    8.000 /tmp/test.py:95(function_to_profile)
#         2    2.000    2.000    3.000    3.000 /tmp/test.py:89(sub_function)
#         3    1.000    1.000    1.000    1.000 /tmp/test.py:89(sub_function2)
#    "
# )

```
profile-wrapper 1.0.0
Wrapper script for easier use of the built-in profiler
Requires Python: >=3.3

# Python3 - Profiler Wrapper

This package provides a wrapper function for easier usage of the built-in profiler.

It only uses builtin modules.

## Install

```bash
python3 -m pip install profiler_wrapper
```

See: [PyPI](https://pypi.org/project/profiler_wrapper/)

## Install

```bash
pip install profiler_wrapper
```

## Usage

```python3
from profiler_wrapper import profile

def function_to_profile(switch: bool, data: dict, msg: str):
    ...
    return {'data': 'test'}


profile(
    target=function_to_profile,
    args=[True],
    kwargs={
        'data': {'random': 'test'},
        'msg': 'Yesterday is gone.'
    },
    lines=3,
)

# (
#   {'data': 'test'},
#
#   "270011472 function calls (270011215 primitive calls) in 181.830 seconds
#    Ordered by: internal time
#
#    ncalls  tottime  percall  cumtime  percall filename:lineno(function)
#         1    5.000    5.000    8.000    8.000 /tmp/test.py:95(function_to_profile)
#         2    2.000    2.000    3.000    3.000 /tmp/test.py:89(sub_function)
#         3    1.000    1.000    1.000    1.000 /tmp/test.py:89(sub_function2)
#    "
# )

```