Merge pull request #55 from flatironinstitute/nemos

converted to nemos

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

@@ -0,0 +1,75 @@
+name: Build and upload to PyPI for pure python
+on:
+  release:
+    types: [published]
+
+jobs:
+  build:
+    name: Build and test package
+    runs-on: ubuntu-latest
+    steps:
+      - uses: actions/checkout@v3
+        # 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
+        with:
+          fetch-depth: 0
+      - name: Set up Python
+        uses: actions/setup-python@v4
+        with:
+          python-version: '3.x'
+      - name: Build package
+        run: |
+          pip install build
+          python -m build --outdir dist/ --sdist --wheel
+      - name: Check there's only one sdist and one whl file created
+        shell: bash
+        # because the following two tests will be weird otherwise. see
+        # https://askubuntu.com/a/454568 for why this is the right way to handle
+        # it. using [[ BOOLEAN ]] || EXPR is a compact way of writing IF NOT
+        # BOOLEAN THEN EXPR in bash
+        run: |
+          [[ $(find dist/ -type f -name "*whl" -printf x | wc -c) == 1 ]] || exit 1
+          [[ $(find dist/ -type f -name "*tar.gz" -printf x | wc -c) == 1 ]] || exit 1
+      - name: Check setuptools_scm version against git tag
+        shell: bash
+        run: |
+          # we use the error code of this comparison: =~ is bash's regex
+          # operator, so it checks whether the right side is contained in the
+          # left side. In particular, we succeed if the path of the source code
+          # ends in the most recent git tag, fail if it does not.
+          [[ "$(ls dist/*tar.gz)" =~ "-$(git describe --tags).tar.gz" ]]
+      - name: Check we can install from wheel
+        # note that this is how this works in bash (different shells might be
+        # slightly different). we've checked there's only one .whl file in an
+        # earlier step, so the bit in `$()` will expand to that single file,
+        # then we pass [dev] to get specify the optional dev dependencies, and
+        # we wrap the whole thing in quotes so bash doesn't try to interpret the
+        # square brackets but passes them directly to pip install
+        shell: bash
+        run: |
+          pip install "$(ls dist/*whl)[dev]"
+      - name: Run some tests
+        # modify the following as necessary to e.g., run notebooks
+        run: |
+          pytest
+      - uses: actions/upload-artifact@v3
+        with:
+          path: dist/*
+
+  publish:
+    name: Upload release to Test PyPI
+    needs: [build]
+    environment: pypi
+    runs-on: ubuntu-latest
+    permissions:
+      id-token: write # IMPORTANT: this permission is mandatory for trusted publishing
+    steps:
+      - uses: actions/download-artifact@v3
+        with:
+          name: artifact
+          path: dist
+      - name: Publish package to test pypi
+        uses: pypa/gh-action-pypi-publish@release/v1
+        with:
+          repository-url: https://test.pypi.org/legacy/

CONTRIBUTING.md

@@ -1,6 +1,6 @@
 # Contributing
 
-The `neurostatslib` package is designed to provide a robust set of statistical analysis tools for neuroscience research. While the repository is managed by a core team of data scientists at the Center for Computational Neuroscience of the Flatiron Institute, we warmly welcome contributions from external collaborators.
+The `nemos` package is designed to provide a robust set of statistical analysis tools for neuroscience research. While the repository is managed by a core team of data scientists at the Center for Computational Neuroscience of the Flatiron Institute, we warmly welcome contributions from external collaborators.
 
 ## General Guidelines
 

README.md

@@ -1,15 +1,18 @@
 ![LOGO](CCN-logo-wText.png)
 
-# neurostatslib
-A toolbox of statistical analysis for neuroscience. 
+# nemos
+NEural MOdelS, a statistical modeling framework for neuroscience.
+
+## Disclaimer
+This is an alpha version, the code is in active development and the API is subject to change.
 
 ## Setup
 
 To install, clone this repo and install using `pip`:
 
 ``` sh
-git clone [email protected]:flatironinstitute/generalized-linear-models.git
-cd generalized-linear-models/
+git clone [email protected]:flatironinstitute/nemos.git
+cd nemos/
 pip install -e .
 ```
 

docs/developers_notes/README.md

@@ -1,6 +1,6 @@
 # Introduction
 
-Welcome to the Developer Notes of the `neurostatslib` project. These notes aim to provide detailed technical information about the various modules, classes, and functions that make up this library, as well as guidelines on how to write code that integrates nicely with our package. They are intended to help current and future developers understand the design decisions, structure, and functioning of the library, and to provide guidance on how to modify, extend, and maintain the codebase.
+Welcome to the Developer Notes of the `nemos` project. These notes aim to provide detailed technical information about the various modules, classes, and functions that make up this library, as well as guidelines on how to write code that integrates nicely with our package. They are intended to help current and future developers understand the design decisions, structure, and functioning of the library, and to provide guidance on how to modify, extend, and maintain the codebase.
 
 ## Intended Audience
 
@@ -10,25 +10,25 @@ These notes are primarily intended for the following groups:
 
 - **Future Developers**: These notes can help onboard new developers to the project, providing them with detailed explanations of the codebase and its underlying architecture.
 
-- **Contributors**: If you wish to contribute to the `neurostatslib` project, the Developer Notes can provide a solid foundation of understanding, helping to ensure that your contributions align with the existing structure and design principles of the library.
+- **Contributors**: If you wish to contribute to the `nemos` project, the Developer Notes can provide a solid foundation of understanding, helping to ensure that your contributions align with the existing structure and design principles of the library.
 
 - **Advanced Users**: While the primary focus of these notes is on development, they might also be of interest to advanced users who want a deeper understanding of the library's functionality. 
 
 Please note that these notes assume a certain level of programming knowledge. Familiarity with Python, object-oriented programming, and the NumPy, Scipy and Jax libraries would be beneficial when reading these notes.
 
 ## Navigating the Developer Notes
 
-The Developer Notes are divided into sections, each focusing on a different module or class within the `neurostatslib` library. Each section provides an overview of the class or module, explains its role and functionality within the library, and offers a comprehensive guide to its classes and functions. Typically, we will provide instructions on how to extend the existing modules. We generally advocate for the use of inheritance and encourage consistency with the existing codebase. In creating developer instructions, we follow the conventions outlined below:
+The Developer Notes are divided into sections, each focusing on a different module or class within the `nemos` library. Each section provides an overview of the class or module, explains its role and functionality within the library, and offers a comprehensive guide to its classes and functions. Typically, we will provide instructions on how to extend the existing modules. We generally advocate for the use of inheritance and encourage consistency with the existing codebase. In creating developer instructions, we follow the conventions outlined below:
 
 - **Must**: This denotes a requirement. Any method or function that fails to meet the requirement will not be merged.
 - **Should**: This denotes a suggestion. Reasons should be provided if a suggestion is not followed.
 - **May**: This denotes an option that, if implemented, could enhance the user/developer experience but can be overlooked if deemed unnecessary. 
 
 ## Interact with us
 
-If you're considering contributing to the library, first of all, welcome aboard! As a first step, we recommend that you read the [`CONTRIBUTING.md`](https://github.com/flatironinstitute/generalized-linear-models/blob/main/CONTRIBUTING.md) guidelines. These will help you understand how to interact with other contributors and how to submit your changes.
+If you're considering contributing to the library, first of all, welcome aboard! As a first step, we recommend that you read the [`CONTRIBUTING.md`](https://github.com/flatironinstitute/nemos/blob/main/CONTRIBUTING.md) guidelines. These will help you understand how to interact with other contributors and how to submit your changes.
 
-If you have any questions or need further clarification on any of the topics covered in these notes, please don't hesitate to reach out to us. You can do so via the [discussion](https://github.com/flatironinstitute/generalized-linear-models/discussions/landing) forum on GitHub.
+If you have any questions or need further clarification on any of the topics covered in these notes, please don't hesitate to reach out to us. You can do so via the [discussion](https://github.com/flatironinstitute/nemos/discussions/landing) forum on GitHub.
 
 We're looking forward to your contributions and to answering any queries you might have!
 

docs/developers_notes/basis_module.md

@@ -2,7 +2,7 @@
 
 ## Introduction
 
-The `neurostatslib.basis` module provides objects that allow users to construct and evaluate basis functions of various types. The classes are hierarchically organized as follows:
+The `nemos.basis` module provides objects that allow users to construct and evaluate basis functions of various types. The classes are hierarchically organized as follows:
 
 ```
 Abstract Class Basis
@@ -30,7 +30,7 @@ Abstract Class Basis
 
 The super-class `Basis` provides two public methods, [`evaluate`](#the-public-method-evaluate) and [`evaluate_on_grid`](#the-public-method-evaluate_on_grid). These methods perform checks on both the input provided by the user and the output of the evaluation to ensure correctness, and are thus considered "safe". They both make use of the private abstract method `_evaluate` that is specific for each concrete class. See below for more details.
 
-## The Class `neurostatslib.basis.Basis`
+## The Class `nemos.basis.Basis`
 
 ### The Public Method `evaluate`
 
@@ -54,7 +54,7 @@ This method performs the following steps:
 
 ### Abstract Methods
 
-The `neurostatslib.basis.Basis` class has the following abstract methods, which every concrete subclass must implement:
+The `nemos.basis.Basis` class has the following abstract methods, which every concrete subclass must implement:
 
 1. `_evaluate`: Evaluates a basis over some specified samples.
 2. `_check_n_basis_min`: Checks the minimum number of basis functions required. This requirement can be specific to the type of basis.
@@ -65,7 +65,7 @@ The `neurostatslib.basis.Basis` class has the following abstract methods, which
 To write a usable (i.e., concrete, non-abstract) basis object, you
 
 - **Must** inherit the abstract superclass `Basis`
-- **Must** define the `_evaluate` and `_check_n_basis_min` methods with the expected input/output format, see [Code References](../../reference/neurostatslib/basis/) for the specifics.
+- **Must** define the `_evaluate` and `_check_n_basis_min` methods with the expected input/output format, see [Code References](../../reference/nemos/basis/) for the specifics.
 - **Should not** overwrite the `evaluate` and `evaluate_on_grid` methods inherited from `Basis`.
 - **May** inherit any number of abstract intermediate classes (e.g., `SplineBasis`). 
 - **May** reimplement the `_get_samples` method if your basis domain differs from `[0,1]`. However, we recommend mapping the specific basis domain to `[0,1]` whenever possible.

docs/examples/plot_1D_basis_function.py

@@ -14,14 +14,14 @@
 
 import numpy as np
 import matplotlib.pylab as plt
-import neurostatslib as nsl
+import nemos as nmo
 
 # Initialize hyperparameters
 order = 4
 n_basis = 10
 
 # Define the 1D basis function object
-mspline_basis = nsl.basis.MSplineBasis(n_basis_funcs=n_basis, order=order)
+mspline_basis = nmo.basis.MSplineBasis(n_basis_funcs=n_basis, order=order)
 
 # %%
 # Evaluating a Basis
@@ -63,12 +63,12 @@
 # Other Basis Types
 # -----------------
 # Each basis type may necessitate specific hyperparameters for instantiation. For a comprehensive description,
-# please refer to the  [Code References](../../../reference/neurostatslib/basis). After instantiation, all classes
+# please refer to the  [Code References](../../../reference/nemos/basis). After instantiation, all classes
 # share the same syntax for basis evaluation. The following is an example of how to instantiate and
 # evaluate a log-spaced cosine raised function basis.
 
 # Instantiate the basis noting that the `RaisedCosineBasisLog` does not require an `order` parameter
-raised_cosine_log = nsl.basis.RaisedCosineBasisLog(n_basis_funcs=10)
+raised_cosine_log = nmo.basis.RaisedCosineBasisLog(n_basis_funcs=10)
 
 # Evaluate the raised cosine basis at the equi-spaced sample points
 # (same method in all Basis elements)

docs/examples/plot_ND_basis_function.py

@@ -65,11 +65,11 @@
 
 import numpy as np
 import matplotlib.pyplot as plt
-import neurostatslib as nsl
+import nemos as nmo
 
 # Define 1D basis objects
-a_basis = nsl.basis.MSplineBasis(n_basis_funcs=15, order=3)
-b_basis = nsl.basis.RaisedCosineBasisLog(n_basis_funcs=14)
+a_basis = nmo.basis.MSplineBasis(n_basis_funcs=15, order=3)
+b_basis = nmo.basis.RaisedCosineBasisLog(n_basis_funcs=14)
 
 # Define the 2D additive basis object
 additive_basis = a_basis + b_basis
@@ -238,9 +238,9 @@
 T = 10
 n_basis = 8
 
-a_basis = nsl.basis.RaisedCosineBasisLinear(n_basis_funcs=n_basis)
-b_basis = nsl.basis.RaisedCosineBasisLinear(n_basis_funcs=n_basis)
-c_basis = nsl.basis.RaisedCosineBasisLinear(n_basis_funcs=n_basis)
+a_basis = nmo.basis.RaisedCosineBasisLinear(n_basis_funcs=n_basis)
+b_basis = nmo.basis.RaisedCosineBasisLinear(n_basis_funcs=n_basis)
+c_basis = nmo.basis.RaisedCosineBasisLinear(n_basis_funcs=n_basis)
 
 prod_basis_3 = a_basis * b_basis * c_basis
 samples = np.linspace(0, 1, T)

docs/examples/plot_example_convolution.py

@@ -9,7 +9,7 @@
 import numpy as np
 import matplotlib.pylab as plt
 import matplotlib.patches as patches
-import neurostatslib as nsl
+import nemos as nmo
 
 np.random.seed(10)
 ws = 11
@@ -39,7 +39,7 @@
 
 
 # create three filters
-basis_obj = nsl.basis.RaisedCosineBasisLinear(n_basis_funcs=3)
+basis_obj = nmo.basis.RaisedCosineBasisLinear(n_basis_funcs=3)
 _, w = basis_obj.evaluate_on_grid(ws)
 
 plt.plot(w)
@@ -48,7 +48,7 @@
 # the function requires an iterable (one element per trial)
 # and returns a list of convolutions
 
-spk_conv = nsl.utils.convolve_1d_trials(w, [spk, np.zeros((20, 1))])
+spk_conv = nmo.utils.convolve_1d_trials(w, [spk, np.zeros((20, 1))])
 print(f"Shape of spk: {spk.shape}\nShape of w: {w.shape}")
 
 # valid convolution should be of shape n_samples - ws + 1
@@ -72,9 +72,9 @@
 
 
 # pad according to the causal direction of the filter, after squeeze, the dimension is (n_filters, n_samples)
-spk_causal_utils = np.squeeze(nsl.utils.nan_pad_conv(spk_conv, ws, filter_type="causal")[0])
-spk_anticausal_utils = np.squeeze(nsl.utils.nan_pad_conv(spk_conv, ws, filter_type="anti-causal")[0])
-spk_acausal_utils = np.squeeze(nsl.utils.nan_pad_conv(spk_conv, ws, filter_type="acausal")[0])
+spk_causal_utils = np.squeeze(nmo.utils.nan_pad_conv(spk_conv, ws, filter_type="causal")[0])
+spk_anticausal_utils = np.squeeze(nmo.utils.nan_pad_conv(spk_conv, ws, filter_type="anti-causal")[0])
+spk_acausal_utils = np.squeeze(nmo.utils.nan_pad_conv(spk_conv, ws, filter_type="acausal")[0])
 
 
 # %%

docs/index.md

@@ -1,7 +1,7 @@
-# neurostatslib 
+# nemos 
 A toolbox of statistical analysis for neuroscience. 
 
 ## Disclaimer
 Please note that this package is currently under development. While you can download and test the functionalities that are already present, it's important to be aware that the code stability and systematic testing cannot be guaranteed at this stage. 
 
-See our [README](https://github.com/flatironinstitute/generalized-linear-models/blob/main/README.md) for more info.
+See our [README](https://github.com/flatironinstitute/nemos/blob/main/README.md) for more info.

mkdocs.yml

@@ -1,5 +1,5 @@
-site_name: neurostatslib
-repo_url: https://github.com/flatironinstitute/generalized-linear-models
+site_name: nemos
+repo_url: https://github.com/flatironinstitute/nemos
 
 theme:
   name: 'material'                         # The theme name, using the 'material' theme

pyproject.toml

@@ -3,7 +3,7 @@ requires = ["setuptools", "setuptools-scm"]
 build-backend = "setuptools.build_meta"
 
 [project]
-name = "neurostatslib"
+name = "nemos"
 version = "0.1.0"
 authors = [
     {name = "Edoardo Balzani", email = "[email protected]"},
@@ -41,7 +41,7 @@ dependencies = [
 # Configure package discovery for setuptools
 [tool.setuptools.packages.find]
 where = ["src"]             # The directory where package modules are located
-include = ["neurostatslib"] # The specific package(s) to include in the distribution
+include = ["nemos"] # The specific package(s) to include in the distribution
 
 
 # Define optional dependencies for the project
@@ -96,7 +96,7 @@ profile = "black"
 
 # Configure pytest
 [tool.pytest.ini_options]
-addopts = "--cov=neurostatslib"   # Additional options to pass to pytest, enabling coverage for the 'neurostatslib' package
+addopts = "--cov=nemos"   # Additional options to pass to pytest, enabling coverage for the 'nemos' package
 testpaths = ["tests"]             # Specify the directory where test files are located
 
 [tool.coverage.report]

src/neurostatslib/basis.py → src/nemos/basis.py

@@ -12,7 +12,7 @@
 from numpy.typing import ArrayLike, NDArray
 from scipy.interpolate import splev
 
-from neurostatslib.utils import row_wise_kron
+from .utils import row_wise_kron
 
 __all__ = [
     "MSplineBasis",
@@ -736,8 +736,8 @@ def _evaluate(self, sample_pts: NDArray) -> NDArray:
         sample_pts : (number of samples,)
             Spacing for basis functions, holding elements on interval [0,
             1). A good default is
-            ``nsl.sample_points.raised_cosine_log`` for log spacing (as used in
-            [2]_) or ``nsl.sample_points.raised_cosine_linear`` for linear
+            ``nmo.sample_points.raised_cosine_log`` for log spacing (as used in
+            [2]_) or ``nmo.sample_points.raised_cosine_linear`` for linear
             spacing.
 
         Returns

tests/test_basis.py

@@ -6,7 +6,7 @@
 import pytest
 import utils_testing
 
-import neurostatslib.basis as basis
+import nemos.basis as basis
 
 # automatic define user accessible basis and check the methods
 

tests/test_convolution_1d.py

@@ -1,7 +1,7 @@
 import numpy as np
 import pytest
 
-import neurostatslib.utils as utils
+import nemos.utils as utils
 
 
 class Test1DConvolution:

tests/test_glm_runs.py

@@ -1,8 +1,8 @@
 import jax
 import numpy as np
 
-from neurostatslib.basis import MSplineBasis
-from neurostatslib.glm import GLM
+from nemos.basis import MSplineBasis
+from nemos.glm import GLM
 
 
 class DimensionMismatchError(Exception):

tests/test_glm_synthetic.py

@@ -9,9 +9,9 @@
 import matplotlib.pyplot as plt
 import numpy as onp
 
-import neurostatslib as nsl
-from neurostatslib.basis import RaisedCosineBasisLinear
-from neurostatslib.glm import GLM
+import nemos as nmo
+from nemos.basis import RaisedCosineBasisLinear
+from nemos.glm import GLM
 
 
 def test_set_up_glm():