fixed zenodo link in readme, added test of import warnings for modules that depend on cvxpy

Author: pavelkomarov

.github/workflows/test.yml

@@ -17,7 +17,9 @@ jobs:
       - name: tests and coverage
         run: |
           pip install -e .[advanced,dev] coveralls
-          coverage run --source=pynumdiff --omit='pynumdiff/_version.py,pynumdiff/tests/*,pynumdiff/utils/old_pi_cruise_control.py' -m pytest -s
+          coverage run --source=pynumdiff --omit="pynumdiff/_version.py,pynumdiff/tests/*,pynumdiff/utils/old_pi_cruise_control.py" -m pytest -s
+          pip uninstall -y cvxpy
+          coverage run -a --source=pynumdiff -m pytest tests/test_imports.py
           coverage xml
       - uses: coverallsapp/github-action@v2
         with:

README.md

@@ -16,8 +16,8 @@ Python methods for numerical differentiation of noisy data, including multi-obje
         <img src='https://coveralls.io/repos/github/florisvb/PyNumDiff/badge.svg?branch=master' alt='Coverage Status' /></a>
     <a href="https://badge.fury.io/py/pynumdiff">
         <img src="https://badge.fury.io/py/pynumdiff.svg" alt="PyPI"></a>
-    <a href="https://zenodo.org/badge/latestdoi/159711175">
-        <img src="https://zenodo.org/badge/159711175.svg" alt="DOI"></a>
+    <a href="https://doi.org/10.5281/zenodo.6374098">
+        <img src="https://zenodo.org/badge/DOI/10.5281/zenodo.6374098.svg" alt="DOI"></a>
     <a href="https://joss.theoj.org/papers/102257ee4b0142bf49bc18d7c810e9d5">
         <img src="https://joss.theoj.org/papers/102257ee4b0142bf49bc18d7c810e9d5/status.svg"></a>
 </p>

pynumdiff/kalman_smooth/_kalman_smooth.py

@@ -260,16 +260,16 @@ def constant_jerk(x, dt, params=None, options=None, r=None, q=None, forwardbackw
 
 def robustdiff(x, dt, order, log_q, log_r, proc_huberM=6, meas_huberM=0):
     """Perform outlier-robust differentiation by solving the Maximum A Priori optimization problem:
-    :math:`\\argmin_{\\{x_n\\}} \\sum_{n=0}^{N-1} V(R^{-1/2}(y_n - C x_n)) + \\sum_{n=1}^{N-1} J(Q^{-1/2}(x_n - A x_{n-1}))`,
+    :math:`\\text{argmin}_{\\{x_n\\}} \\sum_{n=0}^{N-1} V(R^{-1/2}(y_n - C x_n)) + \\sum_{n=1}^{N-1} J(Q^{-1/2}(x_n - A x_{n-1}))`,
     where :math:`A,Q,C,R` come from an assumed constant derivative model and :math:`V,J` are the :math:`\\ell_1` norm or Huber
     loss rather than the :math:`\\ell_2` norm optimized by RTS smoothing. This problem is convex, so this method calls
-    :code:`convex_smooth`.
+    :code:`convex_smooth`, which in turn forms a sparse CVXPY problem and invokes CLARABEL.
 
     Note that for Huber losses, :code:`M` is the radius where the Huber loss function turns from quadratic to linear. Because
-    all inputs to Huber are normalized by noise level, :math:`q^{1/2}` or :math:`r^{1/2}`, :code:`M` is in units of standard
-    deviation. In other words, this choice affects which portion of inputs are treated as outliers. For example, assuming
-    Gaussian inliers, the portion beyond :math:`M\\sigma` is :code:`outlier_portion = 2*(1 - scipy.stats.norm.cdf(M))`. The
-    inverse of this is :code:`M = scipy.stats.norm.ppf(1 - outlier_portion/2)`. As :math:`M \\to \\infty`, Huber becomes the
+    all loss function inputs are normalized by noise level, :math:`q^{1/2}` or :math:`r^{1/2}`, :code:`M` is in units of inlier
+    standard deviation. In other words, this choice affects which portion of inliers might be treated as outliers. For example,
+    assuming Gaussian inliers, the portion beyond :math:`M\\sigma` is :code:`outlier_portion = 2*(1 - scipy.stats.norm.cdf(M))`.
+    The inverse of this is :code:`M = scipy.stats.norm.ppf(1 - outlier_portion/2)`. As :math:`M \\to \\infty`, Huber becomes the
     1/2-sum-of-squares case, :math:`\\frac{1}{2}\\|\\cdot\\|_2^2`, because the normalization constant of the Huber loss (See
     :math:`c_2` in `section 6 of this paper <https://jmlr.org/papers/volume14/aravkin13a/aravkin13a.pdf>`_, missing a
     :math:`\\sqrt{\\cdot}` term there, see p2700) approaches 1 as :math:`M` increases. Similarly, as :code:`M` approaches 0,

pynumdiff/tests/conftest.py

@@ -1,4 +1,4 @@
-"""Pytest configuration for pynumdiff tests"""
+"""Pytest configuration for pynumdiff tests. This is what enables the --plot and --bounds flags to work."""
 import pytest
 from matplotlib import pyplot
 from collections import defaultdict

pynumdiff/tests/test_diff_methods.py

@@ -134,7 +134,7 @@ def spline_irreg_step(*args, **kwargs): return splinediff(*args, **kwargs)
                [(0, 0), (1, 1), (0, -1), (1, 1)],
                [(0, 0), (3, 3), (0, 0), (3, 3)]],
     savgoldiff: [[(-7, -7), (-13, -14), (0, -1), (0, 0)],
-                 [(-7, -7), (-13, -13), (0, -1), (0, 0)],
+                 [(-7, -8), (-13, -13), (0, -1), (0, 0)],
                  [(-2, -2), (-1, -1), (0, -1), (0, 0)],
                  [(0, -1), (0, 0), (0, 0), (1, 0)],
                  [(1, 1), (2, 2), (1, 1), (2, 2)],

pynumdiff/tests/test_imports.py

@@ -0,0 +1,12 @@
+
+import pytest
+import importlib
+
+cvxpy_dependent_modules = ["", ".total_variation_regularization", ".kalman_smooth",
+                            ".linear_model", ".optimize"]
+
+@pytest.mark.skipif(importlib.util.find_spec("cvxpy"), reason="cvxpy installed")
+@pytest.mark.parametrize("module", cvxpy_dependent_modules)
+def test_import_without_cvxpy_warns(module):
+    with pytest.warns(UserWarning):
+        importlib.import_module("pynumdiff" + module)

pynumdiff/tests/test_utils.py

@@ -1,8 +1,5 @@
-"""
-Unit tests for utility functions
-"""
+"""Unit tests for utility functions"""
 # pylint: skip-file
-
 import numpy as np
 from pynumdiff.utils import utility, evaluate
 from pynumdiff.utils.simulate import sine, triangle, pop_dyn, linear_autonomous, pi_cruise_control, lorenz_x