dgread - Python
Fast reader for dg/dgz dynamic group data files.
Installation
pip install dgread
Optional extras for the converters:
pip install "dgread[pandas]" # dgread.to_pandas
pip install "dgread[awkward]" # dgread.to_awkward
Usage
Basic
import dgread
# Load file - returns a dict of columns
data = dgread.read('session.dgz')
print(data.keys())
# dict_keys(['stimtype', 'response', 'rt', 'em', 'events', ...])
# Flat numeric columns are numpy arrays
print(data['rt'][:5])
# [342. 289. 456. 312. 378.]
# Ragged columns are Python lists with one numpy array per row
for i, em in enumerate(data['em'][:3]):
print(f"Trial {i}: {len(em)} samples")
# Trial 0: 1847 samples
# Trial 1: 923 samples
# Trial 2: 2104 samples
What each column kind comes back as:
| in the file | in Python |
|---|---|
| flat numeric list | numpy.ndarray |
| list of strings | list[str] |
| nested list (one level) | list[numpy.ndarray] |
| nested deeper | list[list[...]] |
pandas: one row per trial
df = dgread.to_pandas('session.dgz')
# Several sessions, concatenated in order
df = dgread.to_pandas(['s1.dgz', 's2.dgz', 's3.dgz'])
df.groupby('stimtype')['rt'].median()
# A ragged column is an object column whose cells are numpy arrays.
# Per-sample work goes through apply:
df['n_samples'] = df['em'].apply(len)
df['peak_v'] = df['em'].apply(lambda em: abs(np.diff(em)).max())
Options: columns=[...] to keep a subset, nested="drop" to leave the
ragged columns out, n_rows= to choose the row count explicitly (see
below).
awkward: ragged columns as real axes
When the question lives inside a ragged column, Awkward Array does the per-sample work across every trial at once, in numpy spelling, with no loop:
import awkward as ak
import numpy as np
a = dgread.to_awkward('session.dgz') # or a list of files
dx = a.em_x[:, 1:] - a.em_x[:, :-1] # per-sample diff, all trials
peak = ak.max(np.hypot(dx, dy), axis=1) # one value per trial
ecc = np.hypot(a.em_x, a.em_y)
t_cross = ak.firsts(a.t[ecc > 2]) # first crossing; None if never
correct = a[a.correct == 1] # trial masks work on records
Compute the per-trial scalar in awkward, then hand it back to pandas for the grouping and plotting:
df['peak_v'] = ak.to_numpy(peak)
Non-rectangular groups
A dynamic group need not be rectangular: a file can carry a 1-element
version list, per-trial columns of length 96, and per-observation
event columns of length 97, side by side. Both converters keep the
columns of one length and drop the rest with a UserWarning naming
them. The default is the most common length; pass n_rows=97 to pick
the other table.
trials = dgread.to_pandas('cells.dgz') # 96 rows, warns
events = dgread.to_pandas('cells.dgz', n_rows=97) # 97 rows, warns
Helpers: dgread.row_count(data), dgread.nested_columns(data),
dgread.scalar_columns(data), dgread.is_nested(column).
Compatibility
dgread_utils (load_session, to_dataframe, print_summary, ...)
still imports and now delegates to the package. New code should use
dgread.to_pandas / dgread.to_awkward directly.
The C extension is dgread._dgread; dgread.dgread, dgread.fromString
and dgread.fromString64 are unchanged.
Building from Source
From the repository root:
cd python
pip install -e .
Requirements:
- C compiler
- Python 3.9+
- NumPy
zlib and lz4 are vendored under ../src and compiled in.
Running Tests
cd python
pip install -e ".[dev,pandas,awkward]"
pytest
The tests use the fixtures in ../tests/data. The pandas and awkward
tests skip themselves if the extra is not installed. The same test suite
runs inside cibuildwheel against every built wheel.
1 maintainer
Metadata
1 maintainer
Release files for dgread 1.2.1
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