This is a library to perform shift-invariant sparse dictionary learning, also known as convolutional sparse coding (CSC), on time-series data. It includes a number of different models:

- univariate CSC
- multivariate CSC
- multivariate CSC with a rank-1 constraint [1]
- univariate CSC with an alpha-stable distribution [2]

A mathematical descriptions of these models is available in the documentation.

To install this package, the easiest way is using `pip`

. It will install this
package and its dependencies. The `setup.py`

depends on `numpy`

and
`cython`

for the installation so it is advised to install them beforehand. To
install this package, please run

```
pip install numpy cython
pip install git+https://github.com/alphacsc/alphacsc.git#egg=alphacsc
```

If you do not have admin privileges on the computer, use the `--user`

flag
with `pip`

. To upgrade, use the `--upgrade`

flag provided by `pip`

.

To check if everything worked fine, you can run:

```
python -c 'import alphacsc'
```

and it should not give any error messages.

Here is an example to present briefly the API:

```
import numpy as np
import matplotlib.pyplot as plt
from alphacsc import BatchCDL
# Define the different dimensions of the problem
n_atoms = 10
n_times_atom = 50
n_channels = 5
n_trials = 10
n_times = 1000
# Generate a random set of signals
X = np.random.randn(n_trials, n_channels, n_times)
# Learn a dictionary with batch algorithm and rank1 constraints.
cdl = BatchCDL(n_atoms, n_times_atom, rank1=True)
cdl.fit(X)
# Display the learned atoms
fig, axes = plt.subplots(n_atoms, 2, num="Dictionary")
for k in range(n_atoms):
axes[k, 0].plot(cdl.u_hat_[k])
axes[k, 1].plot(cdl.v_hat_[k])
axes[0, 0].set_title("Spatial map")
axes[0, 1].set_title("Temporal map")
for ax in axes.ravel():
ax.set_xticklabels([])
ax.set_yticklabels([])
plt.show()
```

Use the github issue tracker to report bugs.

If you use this code in your project, please consider citing our work:

[1] | Dupré La Tour, T., Moreau, T., Jas, M., & Gramfort, A. (2018). Multivariate Convolutional Sparse Coding for Electromagnetic Brain Signals. Advances in Neural Information Processing Systems (NIPS). |

[2] | Jas, M., Dupré La Tour, T., Şimşekli, U., & Gramfort, A. (2017). Learning the Morphology of Brain Signals Using Alpha-Stable Convolutional Sparse Coding. Advances in Neural Information Processing Systems (NIPS), pages 1099–1108. |