# Build and upload to PyPI
To build the package:
```shell
pip install build
python -m build
```
To upload the package:
```shell
pip install twine
python -m twine upload --repository pypi dist/*
```
<!-- # ffysh
[Download example dataset (**DO NOT EXTRACT
**)](https://drive.google.com/file/d/1oEETaG6Ra_Ajq5j2awcEXdwmYbW4d2zg/view?usp=sharing) -->
# General use
Everything you need is stored in the ffysh import or in the ffysh CLI - you don't need to worry about its submodules.
## Create a project and log in to Flockfysh
1. Ensure your current working directory is correct.
2. From the terminal, run these two commands:
```shell
ffysh init
ffysh login
```
3. Log in to Flockfysh using the new browser window and click `Approve` to allow ffysh to use your account.
## Datasets
1. You'll first need to create a Dataset object first, using the 24-character ID of the dataset you want.
If the dataset does not exist, an error will be thrown.
```python
from ffysh import Dataset
dataset = Dataset("some_dataset_id")
```
2. From there, you can create a stream object.
```python
stream = dataset.create_stream()
```
Additional dataset attributes and methods:
```python
# Print dataset ID.
print(dataset.dataset_id)
```
## Streams
Streams are the basic unit of operation in ffysh.
Each stream is a lazy-loaded snapshot of a dataset, and it expands as much as the user needs.
Alternatively, you can use it as if it is a Python list.
1. First, store the ID of the stream for later use.
```python
with open("stream_id.json", "w") as file:
json.dump({"id": stream.stream_id}, file)
```
2. This allows the stream to reload from disk.
```python
from ffysh import Stream
with open("stream_id.json", "w") as file:
stream = Stream.load(json.load(file)["id"]
```
Note that each stream instance can only be created or loaded once, and if it is load twice, the old stream instance
will be returned.
```python
with open("stream_id.json", "w") as file:
stream2 = Stream.load(json.load(file)["id"]
print(stream is stream2) # True
```
3. Stream methods:
```python
from ffysh import Stream
with open("stream_id.json", "w") as file:
stream = Stream.load(json.load(file)["id"]
# Iteration
for asset in stream:
print(asset)
# Stream indexing and slicing
print(stream[0:5:2])
# Loads the next 5 items that has not been loaded yet
# into the cache, and return them. If the stream ends,
# an empty list is returned, and any more calls will raise
# a StopIteration exception.
stream.next_assets(5)
# Expands the stream until the stream ends or is at least 100
# items long by loading more items from the cache.
stream.expand(100)
# Expands the stream until the end.
stream.expand()
# Convert the entire stream to a PyTorch zip.
stream.to_pytorch("./dataset.zip", confidence_level=0.6)
```
## Limitations
1. You must not use a stream in 2 Python programs at a time. If you want to, create a second
stream or quit the first program. Although you still have to re-download labels, images will be
cached in a separate directory, so that multiple streams can access the same asset.
2. Even if assets and their labels are deleted remotely, they will stay intact locally.
We'll introduce a way to discard unused streams and purge redundant images in the future.
Raw data
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"description": "# Build and upload to PyPI\n\nTo build the package: \n```shell\npip install build\npython -m build\n```\n\nTo upload the package: \n```shell\npip install twine\npython -m twine upload --repository pypi dist/*\n```\n\n\n<!-- # ffysh\n\n[Download example dataset (**DO NOT EXTRACT\n**)](https://drive.google.com/file/d/1oEETaG6Ra_Ajq5j2awcEXdwmYbW4d2zg/view?usp=sharing) -->\n\n\n# General use\n\nEverything you need is stored in the ffysh import or in the ffysh CLI - you don't need to worry about its submodules.\n\n## Create a project and log in to Flockfysh\n\n1. Ensure your current working directory is correct.\n2. From the terminal, run these two commands:\n ```shell\n ffysh init\n ffysh login\n ```\n3. Log in to Flockfysh using the new browser window and click `Approve` to allow ffysh to use your account.\n\n## Datasets\n\n1. You'll first need to create a Dataset object first, using the 24-character ID of the dataset you want.\n If the dataset does not exist, an error will be thrown.\n\n ```python\n from ffysh import Dataset\n \n dataset = Dataset(\"some_dataset_id\")\n ```\n\n2. From there, you can create a stream object.\n ```python\n stream = dataset.create_stream()\n ```\n\nAdditional dataset attributes and methods:\n\n```python\n# Print dataset ID.\nprint(dataset.dataset_id)\n```\n\n## Streams\n\nStreams are the basic unit of operation in ffysh.\nEach stream is a lazy-loaded snapshot of a dataset, and it expands as much as the user needs.\nAlternatively, you can use it as if it is a Python list.\n\n1. First, store the ID of the stream for later use.\n\n ```python\n with open(\"stream_id.json\", \"w\") as file:\n json.dump({\"id\": stream.stream_id}, file)\n ```\n\n2. This allows the stream to reload from disk.\n\n ```python\n from ffysh import Stream\n \n with open(\"stream_id.json\", \"w\") as file:\n stream = Stream.load(json.load(file)[\"id\"]\n ```\n\n Note that each stream instance can only be created or loaded once, and if it is load twice, the old stream instance\n will be returned.\n\n ```python\n with open(\"stream_id.json\", \"w\") as file:\n stream2 = Stream.load(json.load(file)[\"id\"]\n \n print(stream is stream2) # True\n ```\n\n3. Stream methods:\n ```python\n from ffysh import Stream\n \n with open(\"stream_id.json\", \"w\") as file:\n stream = Stream.load(json.load(file)[\"id\"]\n \n # Iteration\n for asset in stream:\n print(asset)\n \n # Stream indexing and slicing\n print(stream[0:5:2])\n \n # Loads the next 5 items that has not been loaded yet \n # into the cache, and return them. If the stream ends, \n # an empty list is returned, and any more calls will raise \n # a StopIteration exception.\n stream.next_assets(5)\n \n # Expands the stream until the stream ends or is at least 100 \n # items long by loading more items from the cache.\n stream.expand(100)\n \n # Expands the stream until the end.\n stream.expand()\n \n # Convert the entire stream to a PyTorch zip.\n stream.to_pytorch(\"./dataset.zip\", confidence_level=0.6)\n ```\n\n## Limitations\n1. You must not use a stream in 2 Python programs at a time. If you want to, create a second \nstream or quit the first program. Although you still have to re-download labels, images will be \ncached in a separate directory, so that multiple streams can access the same asset.\n2. Even if assets and their labels are deleted remotely, they will stay intact locally.\nWe'll introduce a way to discard unused streams and purge redundant images in the future.\n",
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