@@ -4197,6 +4197,53 @@ (define-public python-pomegranate
Cython for speed.")
(license license:expat)))
+(define-public python-daft
+ (package
+ (name "python-daft")
+ (version "0.1.2")
+ (source (origin
+ (method url-fetch)
+ (uri (pypi-uri "daft" version))
+ (sha256
+ (base32
+ "1r8jsfavd624q2q61f863lk6has6mv5csswh39saafd5khwf0xry"))))
+ (build-system pyproject-build-system)
+ (arguments
+ (list
+ #:test-flags
+ '(list "-k"
+ (string-append
+ ;; The following tests compare matplotlib output with
+ ;; previously generated images. They fail due to minor
+ ;; differences in where matplotlib places labels.
+ "not test_bca[png]"
+ " and not test_classic[png]"
+ " and not test_deconvolution[png]"
+ " and not test_exoplanets[png]"
+ " and not test_fixed[png]"
+ " and not test_gaia[png]"
+ " and not test_galex[png]"
+ " and not test_huey_p_newton[png]"
+ " and not test_logo[png]"
+ " and not test_no_circles[png]"
+ " and not test_no_gray[png]"
+ " and not test_recursive[png]"
+ " and not test_thick_lines[png]"
+ " and not test_weaklensing[png]"
+ " and not test_wordy[png]"))))
+ (propagated-inputs (list python-matplotlib python-numpy))
+ (native-inputs (list python-pytest))
+ (home-page "https://docs.daft-pgm.org/")
+ (synopsis "PGM rendering library")
+ (description "Daft is a Python package that uses matplotlib to
+render pixel-perfect probabilistic graphical models for publication in
+a journal or on the internet. With a short Python script and an
+intuitive model-building syntax you can design directed (Bayesian
+Networks, directed acyclic graphs) and undirected (Markov random fields)
+models and save them in any formats that matplotlib supports (including
+PDF, PNG, EPS and SVG).")
+ (license license:expat)))
+
(define-public python-portalocker
(package
(name "python-portalocker")