Merge pull request #27 from derb12/2D

ENH: Add 2D versions of several baseline algorithms

.github/PULL_REQUEST_TEMPLATE.md

@@ -5,9 +5,8 @@
 are required and/or what problems they fix. Link or add the issue number
 for any existing issues that the pull request solves.
 
-Note that unsolicited pull requests will most likely be closed.
-Please file an issue first, so that details can be discussed/finalized
-before a pull request is created.-->
+Note that it is preferred to file an issue first, so that details can be
+discussed/finalized before a pull request is created.-->
 
 
 ### Type of Pull Request
@@ -24,9 +23,9 @@ before a pull request is created.-->
 To run tests locally, type the following command within the pybaselines
 directory: pytest .
 
-To lint files using flake8 to see if they pass PEP 8 standards and that
+To lint files using ruff to see if they pass PEP 8 standards and that
 docstrings are okay, run the following command within the pybaselines
-directory: flake8 .
+directory: ruff check .
 
 To build documentation locally, type the following command within the
 docs directory: make html

.github/workflows/python-test-latest.yml

@@ -0,0 +1,54 @@
+# For testing the nightly builds of numpy and scipy so that any new changes will not be
+# a surprise.
+
+# Will only trigger if there is a change within pybaselines or tests directories.
+
+name: test-latest-dependencies
+
+on:
+  # allow manually activating the workflow
+  workflow_dispatch:
+
+  push:
+    branches: [ main ]
+    paths:
+    - 'pybaselines/**'
+    - 'tests/**'
+    - '.github/workflows/**'
+
+  pull_request:
+    # always trigger on a pull request, regardless of the branch
+    paths:
+    - 'pybaselines/**'
+    - 'tests/**'
+    - '.github/workflows/**'
+
+jobs:
+  test-nightly:
+
+    runs-on: ubuntu-latest
+    strategy:
+      fail-fast: false
+      matrix:
+        # Choose the latest stable python version
+        python-version: ['3.12']
+
+    steps:
+    - uses: actions/checkout@v4
+
+    - name: Set up Python ${{ matrix.python-version }}
+      uses: actions/setup-python@v5
+      with:
+        python-version: ${{ matrix.python-version }}
+
+    - name: Install required dependencies
+      run: |
+        python -m pip install --upgrade pip
+        python -m pip install pytest
+        python -m pip install -i https://pypi.anaconda.org/scientific-python-nightly-wheels/simple numpy
+        python -m pip install -i https://pypi.anaconda.org/scientific-python-nightly-wheels/simple scipy
+
+    - name: Test with required dependencies
+      # use -Werror so that any warnings will show up as errors -> want to be as stringent
+      # as possible
+      run: python -Werror -m pytest .

.github/workflows/python-test.yml

@@ -26,87 +26,88 @@ on:
 jobs:
   test:
 
-    runs-on: ubuntu-20.04
+    runs-on: ubuntu-latest
     strategy:
       fail-fast: false
       matrix:
         # Use strings since yaml considers 3.10 equal to 3.1
-        python-version: ['3.6', '3.7', '3.8', '3.9', '3.10', '3.11', '3.12']
+        python-version: ['3.8', '3.9', '3.10', '3.11', '3.12']
 
     steps:
-    - uses: actions/checkout@v3
+    - uses: actions/checkout@v4
 
     - name: Set up Python ${{ matrix.python-version }}
-      uses: actions/setup-python@v4
+      uses: actions/setup-python@v5
       with:
         python-version: ${{ matrix.python-version }}
 
     - name: Install required dependencies
       run: |
         python -m pip install --upgrade pip
-        python -m pip install "numpy>=1.14" "scipy>=1.0" pytest
-
-    # Only lint a single version; pick a recent, stable version
-    - name: Install linting dependencies
-      id: install-linters
-      if: matrix.python-version == '3.10'
-      run: |
-        python -m pip install flake8 flake8-comprehensions flake8-docstrings
-
-    - name: Lint
-      if: steps.install-linters.outcome == 'success'
-      run: |
-        # stop the build if there are Python syntax errors or undefined names
-        flake8 . --count --select=E9,F63,F7,F82 --show-source --statistics
-        # exit-zero treats all errors as warnings.
-        flake8 . --count --exit-zero --statistics
+        python -m pip install "numpy>=1.20" "scipy>=1.5" pytest
 
     - name: Test with required dependencies
       run: pytest .
 
     - name: Install optional dependencies
       id: install-optional
-      # uncomment below in case this step ever needs skipped again
-      if: matrix.python-version != '3.12'
-      run: python -m pip install "pentapy>=1.0" "numba>=0.45"
+      # uncomment below to allow skipping future versions
+      #if: matrix.python-version != '3.13'
+      run: python -m pip install "pentapy>=1.0" "numba>=0.49"
 
     - name: Test with optional dependencies
-      # uncomment below in case this step ever needs skipped again
       if: steps.install-optional.outcome == 'success'
       run: pytest .
 
 
   test-min-dependencies:
 
-    runs-on: ubuntu-20.04
+    runs-on: ubuntu-latest
     strategy:
       fail-fast: false
       matrix:
-        python-version: ['3.6']
+        python-version: ['3.8']
 
     steps:
-    - uses: actions/checkout@v3
+    - uses: actions/checkout@v4
 
     - name: Set up Python ${{ matrix.python-version }}
-      uses: actions/setup-python@v4
+      uses: actions/setup-python@v5
       with:
         python-version: ${{ matrix.python-version }}
 
     - name: Install minimum dependencies
-      # Use numpy 1.14.5 rather than 1.14.0 since the optional
-      # dependency pentapy requires numpy>=1.14.5; no relevant difference
-      # between 1.14.0 and 1.14.5.
       run: |
         python -m pip install --upgrade pip
-        python -m pip install numpy==1.14.5 scipy==1.0 pytest
+        python -m pip install numpy==1.20 scipy==1.5 pytest
 
     - name: Test with minimum required dependencies
       run: pytest .
 
     - name: Install minimum optional dependencies
-    # Have to pin llvmlite to 0.30.0 since it otherwise gets a more recent
-    # version that is imcompatible with numba v0.45
-      run: python -m pip install pentapy==1.0 numba==0.45 llvmlite==0.30.0
+      run: python -m pip install pentapy==1.0 numba==0.49
 
     - name: Test with minimum optional dependencies
       run: pytest .
+
+  lint:
+
+    runs-on: ubuntu-latest
+    strategy:
+      fail-fast: false
+      matrix:
+        python-version: ['3.11']
+
+    steps:
+    - uses: actions/checkout@v4
+
+    - name: Set up Python ${{ matrix.python-version }}
+      uses: actions/setup-python@v5
+      with:
+        python-version: ${{ matrix.python-version }}
+
+    - name: Install linting dependencies
+      run: python -m pip install ruff
+
+    - name: Lint
+      run: ruff check . --show-source

.readthedocs.yaml

@@ -8,7 +8,7 @@ version: 2
 build:
   os: ubuntu-22.04
   tools:
-    python: "3.9"
+    python: "3.11"
 
 # Path to sphinx's configuration file
 sphinx:

docs/Makefile

@@ -4,7 +4,6 @@
 # You can set these variables from the command line.
 SPHINXOPTS    =
 SPHINXBUILD   = python -msphinx
-SPHINXPROJ    = mcetl
 SOURCEDIR     = .
 BUILDDIR      = _build
 

docs/_templates/autoapi/python/package.rst

@@ -14,22 +14,6 @@
 
 {% endif %}
 
-{% block subpackages %}
-{% set visible_subpackages = obj.subpackages|selectattr("display")|list %}
-{% if visible_subpackages %}
-Subpackages
------------
-.. toctree::
-   :titlesonly:
-   :maxdepth: 3
-
-{% for subpackage in visible_subpackages %}
-   {{ subpackage.short_name }}/index.rst
-{% endfor %}
-
-
-{% endif %}
-{% endblock %}
 {% block submodules %}
 {% set visible_submodules = obj.submodules|selectattr("display")|list %}
 {% if visible_submodules %}
@@ -47,6 +31,24 @@ Submodules
 
 {% endif %}
 {% endblock %}
+
+{% block subpackages %}
+{% set visible_subpackages = obj.subpackages|selectattr("display")|list %}
+{% if visible_subpackages %}
+Subpackages
+-----------
+.. toctree::
+   :titlesonly:
+   :maxdepth: 3
+
+{% for subpackage in visible_subpackages %}
+   {{ subpackage.short_name }}/index.rst
+{% endfor %}
+
+
+{% endif %}
+{% endblock %}
+
 {% block content %}
 {% if obj.all is not none %}
 {% set visible_children = obj.children|selectattr("short_name", "in", obj.all)|list %}

docs/algorithms/classification.rst

@@ -65,7 +65,7 @@ Algorithms
 dietrich (Dietrich's Classification Method)
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
-:meth:`.dietrich` calculates the power spectrum of the data as the squared derivative
+:meth:`~.Baseline.dietrich` calculates the power spectrum of the data as the squared derivative
 of the data. Then baseline points are identified by iteratively removing points where
 the mean of the power spectrum is less a multiple of the standard deviation of the
 power spectrum. The baseline is created by first interpolating through all baseline
@@ -197,7 +197,7 @@ points, and then iteratively fitting a polynomial to the interpolated baseline.
 golotvin (Golotvin's Classification Method)
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
-:meth:`.golotvin` divides the data into sections and takes the minimum standard
+:meth:`~.Baseline.golotvin` divides the data into sections and takes the minimum standard
 deviation of all the sections as the noise's standard deviation for the entire data.
 Then classifies any point where the rolling max minus min is less than a multiple of
 the noise's standard deviation as belonging to the baseline.
@@ -224,7 +224,7 @@ the noise's standard deviation as belonging to the baseline.
 std_distribution (Standard Deviation Distribution)
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
-:meth:`.std_distribution` identifies baseline segments by analyzing the rolling
+:meth:`~.Baseline.std_distribution` identifies baseline segments by analyzing the rolling
 standard deviation distribution. The rolling standard deviations are split into two
 distributions, with the smaller distribution assigned to noise. Baseline points are
 then identified as any point where the rolled standard deviation is less than a multiple
@@ -253,8 +253,8 @@ of the median of the noise's standard deviation distribution.
 fastchrom (FastChrom's Baseline Method)
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
-:meth:`.fastchrom` identifies baseline segments by analyzing the rolling standard
-deviation distribution, similar to :meth:`.std_distribution`. Baseline points are
+:meth:`~.Baseline.fastchrom` identifies baseline segments by analyzing the rolling standard
+deviation distribution, similar to :meth:`~.Baseline.std_distribution`. Baseline points are
 identified as any point where the rolling standard deviation is less than the specified
 threshold, and peak regions are iteratively interpolated until the baseline is below the data.
 
@@ -279,7 +279,7 @@ threshold, and peak regions are iteratively interpolated until the baseline is b
 cwt_br (Continuous Wavelet Transform Baseline Recognition)
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
-:meth:`.cwt_br` identifies baseline segments by performing a continous wavelet
+:meth:`~.Baseline.cwt_br` identifies baseline segments by performing a continous wavelet
 transform (CWT) on the input data at various scales, and picks the scale with the first
 local minimum in the Shannon entropy. The threshold for baseline points is obtained by fitting
 a Gaussian to the histogram of the CWT at the optimal scale, and the final baseline is fit
@@ -315,8 +315,8 @@ other points have a weight of 0.
 fabc (Fully Automatic Baseline Correction)
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
-:meth:`.fabc` identifies baseline segments by thresholding the squared first derivative
-of the data, similar to :meth:`.dietrich`. However, fabc approximates the first derivative
+:meth:`~.Baseline.fabc` identifies baseline segments by thresholding the squared first derivative
+of the data, similar to :meth:`~.Baseline.dietrich`. However, fabc approximates the first derivative
 using a continous wavelet transform with the Haar wavelet, which is more robust to noise
 than the numerical derivative in Dietrich's method. The baseline is then fit using
 Whittaker smoothing with all baseline points having a weight of 1 and all other points

docs/algorithms/index.rst

@@ -2,15 +2,23 @@
 Algorithms
 ==========
 
-The currently available baseline correction algorithms in pybaselines are split into
-polynomial, whittaker, morphological, smooth, spline, classification, optimizers,
-and miscellaneous (misc). Note that this is more for grouping code and not meant as
-a hard-classification of the algorithms.
+The currently available baseline correction algorithms in pybaselines can broadly be categorized
+as polynomial, whittaker, morphological, smooth, spline, classification, optimizers,
+and miscellaneous (misc) methods. Note that this is simply for grouping code and helping to
+explain the internals of this library and **NOT** meant as a hard-classification of the
+field of baseline correction (Please stop blindly copying this section in papers. There are
+numerous types of baseline correction algorithms that are not included within pybaselines, which
+is why baseline correction in general is such an absolutely fascinating field! Besides, miscellaneous
+is obviously not an actual type of baseline correction...)
 
 This section of the documentation is to help provide some context for each algorithm.
 In addition, most algorithms will have a figure that shows how well the algorithm fits
-various baselines to help choose the correct algorithm for a particular baseline. Refer
-to the :doc:`API section <../api/index>` of the documentation for the full parameter and
+various datasets to help choose the correct algorithm for a particular baseline. These datasets
+include noisy data, data with both positive and negative peaks, data with overlapping peaks,
+and concave data, and they serve as a way to quickly filter out algorithms that would not
+work for a particular dataset.
+
+Refer to the :doc:`API section <../api/index>` of the documentation for the full parameter and
 reference listing for any algorithm.