# arrayfile
A file-backed numeric array using struct.pack. Does not support inserts or
slicing.
Smaller than relying on numpy though.
## Installation
```bash
pip install arrayfile
```
## Usage
### Temporary Array
This creates an array in your temp dir:
```python
from arrayfile import Array
# Create a temporary array with float data
arr = Array('f')
arr.append(3.14)
arr.append(2.71)
arr.extend([1.41, 1.73])
print(f"Length: {len(arr)}")
print(f"Values: {[arr[i] for i in range(len(arr))]}")
arr.close() # Clean up resources
```
### Persistent Array
You can use the same file, if you want to persist your data across sessions:
```python
from arrayfile import Array
# Create and populate an array file
arr = Array('i', 'numbers.array', 'w+b')
for i in range(1000):
arr.append(i * 2)
arr.close()
# Reopen the same file later
arr = Array('i', 'numbers.array', 'r+b')
print(f"Array has {len(arr)} elements")
print(f"First element: {arr[0]}")
print(f"Last element: {arr[-1]}")
# Add more data
arr.append(2000)
arr.close()
```
## Context manager
It has a finalizer in case you forget to call `close()`, but if you like to keep
your code tidy, you can use a context manager, like so:
```python
from arrayfile import Array
# Using double precision floats with context manager
with Array('d', 'measurements.array', 'w+b') as arr:
arr.extend([3.141592653589793, 2.718281828459045, 1.4142135623730951])
print(f"Stored {len(arr)} precise measurements")
for i, value in enumerate(arr):
print(f" {i}: {value:.15f}")
```
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"description": "# arrayfile\n\nA file-backed numeric array using struct.pack. Does not support inserts or\nslicing.\n\nSmaller than relying on numpy though.\n\n## Installation\n\n```bash\npip install arrayfile\n```\n\n## Usage\n\n### Temporary Array\n\nThis creates an array in your temp dir:\n\n```python\nfrom arrayfile import Array\n\n# Create a temporary array with float data\narr = Array('f')\narr.append(3.14)\narr.append(2.71)\narr.extend([1.41, 1.73])\n\nprint(f\"Length: {len(arr)}\")\nprint(f\"Values: {[arr[i] for i in range(len(arr))]}\")\narr.close() # Clean up resources\n```\n\n### Persistent Array\n\nYou can use the same file, if you want to persist your data across sessions:\n\n```python\nfrom arrayfile import Array\n\n# Create and populate an array file\narr = Array('i', 'numbers.array', 'w+b')\nfor i in range(1000):\n arr.append(i * 2)\narr.close()\n\n# Reopen the same file later\narr = Array('i', 'numbers.array', 'r+b')\nprint(f\"Array has {len(arr)} elements\")\nprint(f\"First element: {arr[0]}\")\nprint(f\"Last element: {arr[-1]}\")\n\n# Add more data\narr.append(2000)\narr.close()\n```\n\n## Context manager\n\nIt has a finalizer in case you forget to call `close()`, but if you like to keep\nyour code tidy, you can use a context manager, like so:\n\n```python\nfrom arrayfile import Array\n\n# Using double precision floats with context manager\nwith Array('d', 'measurements.array', 'w+b') as arr:\n arr.extend([3.141592653589793, 2.718281828459045, 1.4142135623730951])\n\n print(f\"Stored {len(arr)} precise measurements\")\n for i, value in enumerate(arr):\n print(f\" {i}: {value:.15f}\")\n```\n\n\n",
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