Fourier

.github/workflows/ci.yml

@@ -81,12 +81,28 @@ jobs:
       - name: Check links
         run: ./bash_scripts/prevent_absolute_links_to_docs.sh
 
+  check-links:
+      runs-on: ubuntu-latest
+      steps:
+        - name: Checkout repository
+          uses: actions/checkout@v4
+
+        - name: Install markdown-link-check
+          run: npm install -g markdown-link-check
+
+        - name: Make .sh executable
+          run: chmod +x bash_scripts/check_markdown_links.sh
+
+        - name: Check links in root Markdown files
+          run: ./bash_scripts/check_markdown_links.sh
+
   check:
     if: ${{ !github.event.pull_request.draft }}
     needs:
       - tox_tests
       - prevent_docs_absolute_links
       - tox_check
+      - check-links
     runs-on: ubuntu-latest
     steps:
       - name: Decide whether all tests and notebooks succeeded

.github/workflows/deploy-pure-python.yml

@@ -8,7 +8,7 @@ jobs:
     name: Build and test package
     runs-on: ubuntu-latest
     steps:
-      - uses: actions/checkout@v3
+      - uses: actions/checkout@v4
         # this is necessary for setuptools_scm to work properly with github
         # actions, see https://github.com/pypa/setuptools_scm/issues/480 and
         # https://stackoverflow.com/a/68959339
@@ -70,4 +70,4 @@ jobs:
           name: artifact
           path: dist
       - name: Publish package to pypi
-        uses: pypa/gh-action-pypi-publish@release/v1
+        uses: pypa/gh-action-pypi-publish@release/v1

CONTRIBUTING.md

@@ -9,7 +9,7 @@ Developers are encouraged to contribute to various areas of development. This co
 
 Feel free to work on any section of code that you believe you can improve. More importantly, remember to thoroughly test all your classes and functions, and to provide clear, detailed comments within your code. This not only aids others in using the library, but also facilitates future maintenance and further development.
 
-For more detailed information about NeMoS modules, including design choices and implementation details, visit the [`For Developers`](https://nemos.readthedocs.io/en/latest/developers_notes/) section of the package documentation.
+For more detailed information about NeMoS modules, including design choices and implementation details, visit the [`For Developers`](https://nemos.readthedocs.io/en/latest/developers_notes/README.html) section of the package documentation.
 
 ## Contributing to the code
 
@@ -46,7 +46,7 @@ pip install -e .[dev]
 ```
 
 > [!NOTE]
-> In order to install `nemos` in editable mode you will need a Python virtual environment. Please see our documentation [here](https://nemos.readthedocs.io/en/latest/installation/) that provides guidance on how to create and activate a virtual environment.
+> In order to install `nemos` in editable mode you will need a Python virtual environment. Please see our documentation [here](https://nemos.readthedocs.io/en/latest/installation.html) that provides guidance on how to create and activate a virtual environment.
 
 3) Add the upstream branch:
 
@@ -184,7 +184,7 @@ it must have an `.py` extension, and it must be contained within the `tests` dir
 add it to the tests-to-run.
 
 > [!NOTE]
-> If you have many variants on a test you wish to run, you should make use of pytest's `parameterize` mark. See the official documentation [here](https://docs.pytest.org/en/stable/how-to/parametrize.html) and NeMoS [`test_error_invalid_entry`](https://github.com/flatironinstitute/nemos/blob/main/tests/test_vallidation.py#L27) for a concrete implementation.
+> If you have many variants on a test you wish to run, you should make use of pytest's `parameterize` mark. See the official documentation [here](https://docs.pytest.org/en/stable/how-to/parametrize.html) and NeMoS [`test_error_invalid_entry`](https://github.com/flatironinstitute/nemos/blob/main/tests/test_validation.py) for a concrete implementation.
 
 > [!NOTE]
 > If you are using an object that gets used in multiple tests (such as a model with certain data, regularizer, or solver), you should use pytest's `fixtures` to avoid having to load or instantiate the object multiple times. Look at our `conftest.py` to see already available fixtures for your tests. See the official documentation [here](https://docs.pytest.org/en/stable/how-to/fixtures.html).

README.md

@@ -20,7 +20,7 @@ We provide a **Poisson GLM** for analyzing spike counts, and a **Gamma GLM** for
 The package is under active development and more methods will be added in the future.
 
 For those looking to get a better grasp of the Generalized Linear Model, we recommend checking out the 
-Neuromatch Academy's lesson [here](https://compneuro.neuromatch.io/tutorials/W1D3_GeneralizedLinearModels/student/W1D3_Tutorial1.htmls) and Jonathan Pillow's tutorial 
+Neuromatch Academy's lesson [here](https://compneuro.neuromatch.io/tutorials/W1D3_GeneralizedLinearModels/student/W1D3_Tutorial1.html) and Jonathan Pillow's tutorial 
 from Cosyne 2018 [here](https://www.youtube.com/watch?v=NFeGW5ljUoI&t=424s).
 
 ## Overview
@@ -65,9 +65,9 @@ In this example, we'll construct a time-series of features using the basis objec
 import nemos as nmo
 
 # Instantiate the basis
-basis_1 = nmo.basis.MSplineBasis(n_basis_funcs=5)
-basis_2 = nmo.basis.CyclicBSplineBasis(n_basis_funcs=6)
-basis_3 = nmo.basis.MSplineBasis(n_basis_funcs=7)
+basis_1 = nmo.basis.MSplineEval(n_basis_funcs=5)
+basis_2 = nmo.basis.CyclicBSplineEval(n_basis_funcs=6)
+basis_3 = nmo.basis.MSplineEval(n_basis_funcs=7)
 
 basis = basis_1 * basis_2 + basis_3
 
@@ -99,7 +99,7 @@ ll = glm.score(X, y)
 <img src="docs/assets/glm_population_scheme.svg" width="84%">
 
 This second example demonstrates feature construction by convolving the simultaneously recorded population spike counts with a bank of filters, utilizing the basis in `conv` mode.
-The figure above show the GLM scheme for a single neuron, however in NeMoS you can fit jointly the whole population with the [`PopulationGLM`](https://nemos.readthedocs.io/en/latest/generated/how_to_guide/plot_04_population_glm/) object.
+The figure above show the GLM scheme for a single neuron, however in NeMoS you can fit jointly the whole population with the [`PopulationGLM`](https://nemos.readthedocs.io/en/latest/how_to_guide/plot_03_population_glm.html) object.
 
 #### Feature Representation
 
@@ -111,7 +111,7 @@ import nemos as nmo
 
 # generate 5 basis functions of 100 time-bins, 
 # and convolve the counts with the basis.
-X = nmo.basis.RaisedCosineBasisLog(5, mode="conv", window_size=100
+X = nmo.basis.RaisedCosineLogConv(5, window_size=100
     ).compute_features(spike_counts)
 ```
 #### Population GLM
@@ -127,8 +127,8 @@ firing_rate = glm.predict(X)
 ll = glm.score(X, spike_counts)
 ```
 
-For a deeper dive, see our [Quickstart](https://nemos.readthedocs.io/en/latest/quickstart/)  guide and consider using [pynapple](https://github.com/pynapple-org/pynapple) for data exploration and preprocessing. When initializing the GLM object, you may optionally specify an [observation
-model](https://nemos.readthedocs.io/en/latest/reference/nemos/observation_models/) and a [regularizer](https://nemos.readthedocs.io/en/latest/reference/nemos/regularizer/).
+For a deeper dive, see our [Quickstart](https://nemos.readthedocs.io/en/latest/quickstart.html)  guide and consider using [pynapple](https://github.com/pynapple-org/pynapple) for data exploration and preprocessing. When initializing the GLM object, you may optionally specify an [observation
+model](https://nemos.readthedocs.io/en/latest/api_reference.html#the-nemos-observation-models-module) and a [regularizer](https://nemos.readthedocs.io/en/latest/api_reference.html#the-nemos-regularizer-module).
 
 > **Note: Multi-epoch Convolution**
 >
@@ -149,7 +149,7 @@ Run the following `pip` command in your virtual environment.
  python -m pip install nemos
  ```
 
-For more details, including specifics for GPU users and developers, refer to NeMoS [docs](https://nemos.readthedocs.io/en/latest/installation/).
+For more details, including specifics for GPU users and developers, refer to NeMoS [docs](https://nemos.readthedocs.io/en/latest/installation.html).
 
 
 ## Disclaimer

bash_scripts/check_markdown_links.sh

@@ -0,0 +1,35 @@
+#!/bin/bash
+
+# Fail script if any command fails
+set -e
+
+# Find markdown-link-check path
+MARKDOWN_LINK_CHECK=$(which markdown-link-check || echo "")
+
+# If markdown-link-check is not found, print an error and exit
+if [[ -z "$MARKDOWN_LINK_CHECK" ]]; then
+    echo "❌ ERROR: markdown-link-check command not found. Make sure it is installed globally."
+    exit 1
+fi
+
+echo "🔍 Checking Markdown links in root directory..."
+
+# Initialize an error flag
+ERROR=0
+LOG_FILE=$(mktemp)  # Temporary file to store output
+
+# Find all Markdown files in the root directory and check links
+for file in $(find . -maxdepth 1 -name "*.md"); do
+    echo "📂 Checking $file..."
+
+    # Run markdown-link-check and capture output
+    $MARKDOWN_LINK_CHECK "$file" 2>&1 | tee -a "$LOG_FILE"
+done
+
+# Check if "ERROR:" appears in the log file
+if grep -q "ERROR:" "$LOG_FILE"; then
+    echo "🚨 Link check failed! Please fix broken links."
+    exit 1
+else
+    echo "✅ All links are valid."
+fi

src/nemos/basis/__init__.py

@@ -5,6 +5,7 @@
     BSplineEval,
     CyclicBSplineConv,
     CyclicBSplineEval,
+    FourierEval,
     HistoryConv,
     IdentityEval,
     MSplineConv,

src/nemos/basis/_fourier_basis.py

@@ -0,0 +1,162 @@
+import abc
+from typing import Optional, Tuple
+
+import numpy as np
+from numpy.typing import ArrayLike, NDArray
+from pynapple import Tsd, TsdFrame, TsdTensor
+
+from ..type_casting import support_pynapple
+from ..typing import FeatureMatrix
+from ._basis import Basis, check_transform_input, min_max_rescale_samples
+from ._basis_mixin import AtomicBasisMixin
+
+
+class FourierBasis(Basis, AtomicBasisMixin, abc.ABC):
+    """Fourier Basis.
+
+    Parameters
+    ----------
+    n_basis_funcs :
+        The number of basis functions.
+    include_constant:
+        Include the constant term, which corresponds to 0 frequency. Default is False.
+    mode :
+        The mode of operation. 'eval' for evaluation at sample points,
+        'conv' for convolutional operation.
+    label :
+        The label of the basis, intended to be descriptive of the task variable being processed.
+        For example: velocity, position, spike_counts.
+
+    """
+
+    def __init__(
+        self,
+        n_frequencies: int,
+        include_constant: bool = False,
+        mode="eval",
+        label: Optional[str] = "RaisedCosineBasisLinear",
+    ) -> None:
+
+        self.include_constant = include_constant
+        self._n_input_dimensionality = 1
+
+        # this sets the _n_basis_funcs too
+        self.n_frequencies = n_frequencies
+
+        AtomicBasisMixin.__init__(self, n_basis_funcs=self._n_basis_funcs)
+        super().__init__(
+            mode=mode,
+            label=label,
+        )
+
+    @property
+    def include_constant(self):
+        return bool(self._include_constant)
+
+    @include_constant.setter
+    def include_constant(self, value):
+        if not isinstance(value, bool):
+            raise TypeError(
+                f"`include_constant` must be a boolean. {value} provided instead!"
+            )
+        # store as int (used in slicing)
+        self._include_constant = int(value)
+
+    @support_pynapple(conv_type="numpy")
+    @check_transform_input
+    def _evaluate(  # call these _evaluate
+        self,
+        sample_pts: ArrayLike | Tsd | TsdFrame | TsdTensor,
+    ) -> FeatureMatrix:
+        """Generate basis functions with given samples.
+
+        Parameters
+        ----------
+        sample_pts :
+            Spacing for basis functions, holding elements on interval [0, 1].
+            `sample_pts` is a n-dimensional (n >= 1) array with first axis being the samples, i.e.
+            `sample_pts.shape[0] == n_samples`.
+
+        Raises
+        ------
+        ValueError
+            If the sample provided do not lie in [0,1].
+
+        """
+
+        # scale to 0, 1
+        sample_pts: NDArray = min_max_rescale_samples(
+            np.copy(sample_pts), getattr(self, "bounds", None)
+        )[0]
+        # first sample in 0, last sample in 2 pi - 2 pi / n_samples.
+        sample_pts *= 2 * np.pi * (1.0 - 1.0 / sample_pts.shape[0])
+
+        # reshape samples
+        shape = sample_pts.shape
+        sample_pts = sample_pts.reshape(
+            -1,
+        )
+
+        # compute angles
+        angles = np.outer(sample_pts, self._frequencies)
+        out = np.concatenate(
+            [np.cos(angles), -np.sin(angles[:, int(self._include_constant) :])], axis=1
+        )
+        return out.reshape(*shape, out.shape[1])
+
+    def evaluate_on_grid(self, n_samples: int) -> Tuple[NDArray, NDArray]:
+        """Evaluate the basis set on a grid of equi-spaced sample points.
+
+        Parameters
+        ----------
+        n_samples :
+            The number of points in the uniformly spaced grid. A higher number of
+            samples will result in a more detailed visualization of the basis functions.
+
+        Returns
+        -------
+        X :
+            Array of shape (n_samples,) containing the equi-spaced sample
+            points where we've evaluated the basis.
+        basis_funcs :
+            Fourier basis, shape (n_samples, n_basis_funcs)
+        """
+        return super().evaluate_on_grid(n_samples)
+
+    @property
+    def n_basis_funcs(self) -> tuple | None:
+        """Read-only property for Fourier basis."""
+        return super().n_basis_funcs
+
+    @property
+    def n_frequencies(self) -> int:
+        """Number of frequencies for the basis."""
+        return int(self._frequencies[-1])
+
+    @n_frequencies.setter
+    def n_frequencies(self, value: int):
+        if not isinstance(value, int):
+            raise TypeError(f"`value` must be an integer. {value} provided instead!")
+        if value <= 0:
+            raise ValueError(
+                f"`value` must be a positive integer. {value} provided instead!"
+            )
+        self._n_basis_funcs = 2 * value + self._include_constant
+        self._frequencies = np.arange(
+            1 - self._include_constant, value + 1, dtype=float
+        )
+
+    def _check_n_basis_min(self) -> None:
+        """Check that the user required enough basis elements.
+        Checks that the number of basis is at least 1.
+
+        Raises
+        ------
+        ValueError
+            If a negative number of basis is provided.
+        """
+        if self.n_basis_funcs < 0:
+            raise ValueError(
+                f"Object class {self.__class__.__name__} requires >= 1 basis elements. "
+                f"{self.n_basis_funcs} basis elements specified instead"
+            )