ci: replace ruff with docformatter and updates pre-commit hook versions

.pre-commit-config.yaml

@@ -11,7 +11,7 @@ ci:
   submodules: false
 repos:
   - repo: https://github.com/pre-commit/pre-commit-hooks
-    rev: v4.6.0
+    rev: v6.0.0
     hooks:
       - id: check-yaml
       - id: end-of-file-fixer
@@ -21,45 +21,45 @@ repos:
       - id: check-toml
       - id: check-added-large-files
   - repo: https://github.com/psf/black
-    rev: 24.4.2
+    rev: 26.3.1
     hooks:
       - id: black
   - repo: https://github.com/pycqa/flake8
-    rev: 7.0.0
+    rev: 7.3.0
     hooks:
       - id: flake8
   - repo: https://github.com/pycqa/isort
-    rev: 5.13.2
+    rev: 9.0.0a3
     hooks:
       - id: isort
         args: ["--profile", "black"]
   - repo: https://github.com/kynan/nbstripout
-    rev: 0.7.1
+    rev: 0.9.1
     hooks:
       - id: nbstripout
   - repo: https://github.com/pre-commit/pre-commit-hooks
-    rev: v4.4.0
+    rev: v6.0.0
     hooks:
       - id: no-commit-to-branch
         name: Prevent Commit to Main Branch
         args: ["--branch", "main"]
         stages: [pre-commit]
   - repo: https://github.com/codespell-project/codespell
-    rev: v2.3.0
+    rev: v2.4.2
     hooks:
       - id: codespell
         additional_dependencies:
           - tomli
   # prettier - multi formatter for .json, .yml, and .md files
   - repo: https://github.com/pre-commit/mirrors-prettier
-    rev: f12edd9c7be1c20cfa42420fd0e6df71e42b51ea # frozen: v4.0.0-alpha.8
+    rev: v4.0.0-alpha.8
     hooks:
       - id: prettier
         additional_dependencies:
           - "prettier@^3.2.4"
-  # ruff - An extremely fast Python linter and code formatter, written in Rust.
-  - repo: https://github.com/astral-sh/ruff-pre-commit
-    rev: v0.15.12
+  - repo: https://github.com/PyCQA/docformatter
+    rev: v1.7.8
     hooks:
-      - id: ruff-check
-        args: [--fix]
+      - id: docformatter
+        additional_dependencies: [tomli]
+        args: [--in-place, --config, ./pyproject.toml]

docs/examples/multimodel-known-dG1.py

@@ -13,25 +13,26 @@
 """Extract multiple models from crystalline PDF with known uncertainties
 for use in later AIC-driven multimodeling analysis.
 
-The multimodel approach generates many models of varying complexity by assuming
-a range of experimental uncertainties are physically plausible.  This example
-shows how to generate multiple models from a crystalline silver PDF with
-experimentally determined uncertainties.  The Akaike Information Criterion (AIC)
-will later be used to see which models are relatively more likely to describe
-the experimental data.  For complex PDFs, especially, there are many sets of
-peaks which are physically distinct yet appear to fit the experimental data
-similarly well.  Multimodeling can help determine which models are worth
-investigating first.
-
-NOTE: The multimodeling API used here is expected to change drastically in a
-future version of diffpy.srmise.
-
-For more information on the multimodeling approach taken here see
-[1] Granlund, et al. (2015) Acta Crystallographica A, 71(4), 392-409.
-    doi:10.1107/S2053273315005276
-The standard reference of AIC-based multimodel selection is
-[2] Burnham and Anderson. (2002). Model Selection and Multimodel Inference.
-    New York, NY: Springer. doi:10.1007/b97636
+The multimodel approach generates many models of varying complexity by
+assuming a range of experimental uncertainties are physically plausible.
+This example shows how to generate multiple models from a crystalline
+silver PDF with experimentally determined uncertainties.  The Akaike
+Information Criterion (AIC) will later be used to see which models are
+relatively more likely to describe the experimental data.  For complex
+PDFs, especially, there are many sets of peaks which are physically
+distinct yet appear to fit the experimental data similarly well.
+Multimodeling can help determine which models are worth investigating
+first.
+
+NOTE: The multimodeling API used here is expected to change drastically
+in a future version of diffpy.srmise.
+
+For more information on the multimodeling approach taken here see [1]
+Granlund, et al. (2015) Acta Crystallographica A, 71(4), 392-409.
+doi:10.1107/S2053273315005276 The standard reference of AIC-based
+multimodel selection is [2] Burnham and Anderson. (2002). Model
+Selection and Multimodel Inference.     New York, NY: Springer.
+doi:10.1007/b97636
 """
 
 import numpy as np

docs/examples/multimodel-known-dG2.py

@@ -13,25 +13,26 @@
 """AIC-driven multimodel analysis of crystalline PDF with known
 uncertainties.
 
-The multimodel approach generates many models of varying complexity by assuming
-a range of experimental uncertainties are physically plausible.  This example
-shows how to analyze multiple models obtained (in the previous script) from a
-crystalline silver PDF with experimentally determined uncertainties.  This
-involves calculating the Akaike probabilities, which are a measure of the
-likelihood that a given model is the best model (in the sense of
-Kullback-Leibler divergence) relative to all the other ones in the same
-comparison.
-
-NOTE: The multimodeling API used here is expected to change drastically in a
-future version of diffpy.srmise.
-
-For more information on the multimodeling approach taken here see
-[1] Granlund, et al. (2015) Acta Crystallographica A, 71(4), 392-409.
-    doi:10.1107/S2053273315005276
-The standard reference of AIC-based multimodel selection is
-[2] Burnham and Anderson. (2002). Model Selection and Multimodel Inference.
-    New York, NY: Springer. doi:10.1007/b97636
+The multimodel approach generates many models of varying complexity by
+assuming a range of experimental uncertainties are physically plausible.
+This example shows how to analyze multiple models obtained (in the
+previous script) from a crystalline silver PDF with experimentally
+determined uncertainties.  This involves calculating the Akaike
+probabilities, which are a measure of the likelihood that a given model
+is the best model (in the sense of Kullback-Leibler divergence) relative
+to all the other ones in the same comparison.
+
+NOTE: The multimodeling API used here is expected to change drastically
+in a future version of diffpy.srmise.
+
+For more information on the multimodeling approach taken here see [1]
+Granlund, et al. (2015) Acta Crystallographica A, 71(4), 392-409.
+doi:10.1107/S2053273315005276 The standard reference of AIC-based
+multimodel selection is [2] Burnham and Anderson. (2002). Model
+Selection and Multimodel Inference.     New York, NY: Springer.
+doi:10.1007/b97636
 """
+
 import matplotlib.pyplot as plt
 import numpy as np
 
@@ -73,7 +74,7 @@ def run(plot=True):
     # Nyquist sampling minimizes correlations in the PDF, which is the closest
     # approximation to independence possible for the PDF.
     dr = np.pi / ms.ppe.qmax
-    (r, y, dr2, dy) = ms.ppe.resampledata(dr)
+    r, y, dr2, dy = ms.ppe.resampledata(dr)
 
     # Classify models
     # All models are placed into classes.  Models in the same class
@@ -133,7 +134,7 @@ def run(plot=True):
         # "prob" -> The AIC probability given uncertainty dG
         # These all have dedicated getter functions.  For example, the model
         # index can also be obtained using get_model(dG, corder=i)
-        (model, cls, nfree, aic, prob) = ms.get(dG, "model", "class", "nfree", "aic", "prob", corder=i)
+        model, cls, nfree, aic, prob = ms.get(dG, "model", "class", "nfree", "aic", "prob", corder=i)
 
         filename_base = "output/known_dG_m" + str(model)
 

docs/examples/multimodel-unknown-dG1.py

@@ -13,24 +13,25 @@
 """Extract multiple models from nanoparticle PDF with unknown
 uncertainties for use in later AIC-driven multimodeling analysis.
 
-The multimodel approach generates many models of varying complexity by assuming
-a range of experimental uncertainties are physically plausible.  This example
-shows how to generate multiple models from a C60 nanoparticle PDF with
-unreliable uncertainties.  The Akaike Information Criterion (AIC) will later be
-used to see which models are relatively more likely to describe the experimental
-data.  For complex PDFs, especially, there are many sets of peaks which are
-physically distinct yet appear to fit the experimental data similarly well.
-Multimodeling can help determine which models are worth investigating first.
-
-NOTE: The multimodeling API used here is expected to change drastically in a
-future version of diffpy.srmise.
-
-For more information on the multimodeling approach taken here see
-[1] Granlund, et al. (2015) Acta Crystallographica A, 71(4), 392-409.
-    doi:10.1107/S2053273315005276
-The standard reference of AIC-based multimodel selection is
-[2] Burnham and Anderson. (2002). Model Selection and Multimodel Inference.
-    New York, NY: Springer. doi:10.1007/b97636
+The multimodel approach generates many models of varying complexity by
+assuming a range of experimental uncertainties are physically plausible.
+This example shows how to generate multiple models from a C60
+nanoparticle PDF with unreliable uncertainties.  The Akaike Information
+Criterion (AIC) will later be used to see which models are relatively
+more likely to describe the experimental data.  For complex PDFs,
+especially, there are many sets of peaks which are physically distinct
+yet appear to fit the experimental data similarly well. Multimodeling
+can help determine which models are worth investigating first.
+
+NOTE: The multimodeling API used here is expected to change drastically
+in a future version of diffpy.srmise.
+
+For more information on the multimodeling approach taken here see [1]
+Granlund, et al. (2015) Acta Crystallographica A, 71(4), 392-409.
+doi:10.1107/S2053273315005276 The standard reference of AIC-based
+multimodel selection is [2] Burnham and Anderson. (2002). Model
+Selection and Multimodel Inference.     New York, NY: Springer.
+doi:10.1007/b97636
 """
 
 import numpy as np

docs/examples/multimodel-unknown-dG2.py

@@ -13,31 +13,34 @@
 """AIC-driven multimodel analysis of nanoparticle PDF with unknown
 uncertainties.
 
-The multimodel approach generates many models of varying complexity by assuming
-a range of experimental uncertainties are physically plausible.  This example
-shows how to analyze multiple models obtained from a C60 nanoparticle PDF with
-unreliable uncertainties.  The Akaike Information Criterion (AIC) can be used to
-see which models are relatively more likely to describe the experimental data.
-For complex PDFs, especially, there are many sets of peaks which are physically
-distinct yet appear to fit the experimental data similarly well.  Here we
-calculate the Akaike probabilities, which are a measure of the likelihood that a
-given model is the best model (in the sense of Kullback-Leibler information
+The multimodel approach generates many models of varying complexity by
+assuming a range of experimental uncertainties are physically plausible.
+This example shows how to analyze multiple models obtained from a C60
+nanoparticle PDF with unreliable uncertainties.  The Akaike Information
+Criterion (AIC) can be used to see which models are relatively more
+likely to describe the experimental data. For complex PDFs, especially,
+there are many sets of peaks which are physically distinct yet appear to
+fit the experimental data similarly well.  Here we calculate the Akaike
+probabilities, which are a measure of the likelihood that a given model
+is the best model (in the sense of Kullback-Leibler information
 divergence) relative to all the other ones in the same comparison.  This
-analysis reflects ignorance of the experimental uncertainties by evaluating
-the Akaike probabilities for a range of assumed uncertainties, returning models
-which are selected as best at least once.  This is a weaker analysis than
-possible when the uncertainties are known.
-
-NOTE: The multimodeling API used here is expected to change drastically in a
-future version of diffpy.srmise.
-
-For more information on the multimodeling approach taken here see
-[1] Granlund, et al. (2015) Acta Crystallographica A, 71(4), 392-409.
-    doi:10.1107/S2053273315005276
-The standard reference of AIC-based multimodel selection is
-[2] Burnham and Anderson. (2002). Model Selection and Multimodel Inference.
-    New York, NY: Springer. doi:10.1007/b97636
+analysis reflects ignorance of the experimental uncertainties by
+evaluating the Akaike probabilities for a range of assumed
+uncertainties, returning models which are selected as best at least
+once.  This is a weaker analysis than possible when the uncertainties
+are known.
+
+NOTE: The multimodeling API used here is expected to change drastically
+in a future version of diffpy.srmise.
+
+For more information on the multimodeling approach taken here see [1]
+Granlund, et al. (2015) Acta Crystallographica A, 71(4), 392-409.
+doi:10.1107/S2053273315005276 The standard reference of AIC-based
+multimodel selection is [2] Burnham and Anderson. (2002). Model
+Selection and Multimodel Inference.     New York, NY: Springer.
+doi:10.1007/b97636
 """
+
 import matplotlib.pyplot as plt
 import numpy as np
 
@@ -88,7 +91,7 @@ def run(plot=True):
     # Nyquist sampling minimizes correlations in the PDF, which is the closest
     # approximation to independence possible for the PDF.
     dr = np.pi / ms.ppe.qmax
-    (r, y, dr2, dy) = ms.ppe.resampledata(dr)
+    r, y, dr2, dy = ms.ppe.resampledata(dr)
 
     # Classify models
     # All models are placed into classes.  Models in the same class
@@ -155,7 +158,7 @@ def run(plot=True):
         # "aic" -> The AIC for this model given uncertainty dG
         # "prob" -> The AIC probability given uncertainty dG
         # These all have dedicated getter functions.
-        (model, cls, nfree, aic, prob) = ms.get(dG, "model", "class", "nfree", "aic", "prob")
+        model, cls, nfree, aic, prob = ms.get(dG, "model", "class", "nfree", "aic", "prob")
 
         filename_base = "output/unknown_dG_m" + str(model)
 

docs/examples/query-results.py

@@ -103,7 +103,7 @@ def run(plot=True):
     print("    Covariance(width, area) = ", cov.getcovariance((0, 1), (0, 2)))
 
     # Baseline parameters.  By convention, baseline is final element in cov.
-    (slope, intercept) = cov.model[-1]
+    slope, intercept = cov.model[-1]
     print("\nThe linear baseline B(r)=%f*r + %f" % tuple(par for par in cov.model[-1]))
 
     print("\n ------ Uncertainties from a Saved File --------")
@@ -173,7 +173,7 @@ def run(plot=True):
     # Calculated the scaled intensities and uncertainties.
     intensity = []
     for i in range(0, len(cov.model) - 1):
-        (area, darea) = cov.get((i, 2))
+        area, darea = cov.get((i, 2))
         area *= scale
         darea = area * np.sqrt((dscale / scale) ** 2 + (darea / area) ** 2)
         intensity.append((ideal_intensity[i], area, darea))

news/precommit-hook-update.rst

@@ -0,0 +1,23 @@
+**Added:**
+
+* No news needed: updating docformatter not user facing
+
+**Changed:**
+
+* <news item>
+
+**Deprecated:**
+
+* <news item>
+
+**Removed:**
+
+* <news item>
+
+**Fixed:**
+
+* <news item>
+
+**Security:**
+
+* <news item>

pyproject.toml

@@ -60,11 +60,10 @@ exclude-file = ".codespell/ignore_lines.txt"
 ignore-words = ".codespell/ignore_words.txt"
 skip = "*.cif,*.dat"
 
-[tool.ruff]
-line-length = 115
-
-[tool.ruff.lint.pydocstyle]
-convention = "numpy"
+[tool.docformatter]
+recursive = true
+wrap-summaries = 72
+wrap-descriptions = 72
 
 [tool.black]
 line-length = 115

src/diffpy/srmise/__init__.py

@@ -13,8 +13,7 @@
 #
 ##############################################################################
 """Peak extraction and peak fitting tool for atomic pair distribution
-functions.
-"""
+functions."""
 
 # package version
 from diffpy.srmise.version import __version__  # noqa

src/diffpy/srmise/applications/extract.py

@@ -433,7 +433,7 @@ def main():
     )
     parser.add_option_group(group)
 
-    (options, args) = parser.parse_args()
+    options, args = parser.parse_args()
 
     if len(args) != 1:
         parser.error("Exactly one argument required. \n" + usage)

src/diffpy/srmise/applications/plot.py

@@ -522,7 +522,6 @@ def makeplot(ppe_or_stability, ip=None, **kwds):
 
 
 def on_draw(event):
-    global _lastxpos
     fig = event.canvas.figure
     ax_main = fig.get_axes()[0]
     invisiblelabel = ax_main.axis["left"].label
@@ -556,8 +555,7 @@ def on_draw(event):
 
 def readcompare(filename):
     """Returns a list of distances read from filename, otherwise
-    None.
-    """
+    None."""
     # TODO: Make this safer
     try:
         datastring = open(filename, "rb").read()