Merge pull request #154 from jiangyi15/pre-commit-ci-update-config

[pre-commit.ci] pre-commit autoupdate

.github/workflows/ci.yml

@@ -205,7 +205,7 @@ jobs:
       - name: Install dependencies
         run: |
           python -m pip install --upgrade pip
-          pip install .[test]
+          pip install .[test] numpy==1.26
           python -m pip install uproot3
       - name: Test with pytest
         run: |

.pre-commit-config.yaml

@@ -5,7 +5,7 @@ repos:
       - id: check-useless-excludes
 
   - repo: https://github.com/pre-commit/pre-commit-hooks
-    rev: v3.3.0
+    rev: v5.0.0
     hooks:
       - id: check-ast
       - id: check-case-conflict
@@ -21,32 +21,32 @@ repos:
       - id: trailing-whitespace
 
   - repo: https://github.com/psf/black
-    rev: 22.3.0
+    rev: 24.10.0
     hooks:
       - id: black
         language_version: python3 # Should be a command that runs python3.6+
 
   - repo: https://github.com/PyCQA/doc8
-    rev: 0.8.1
+    rev: v1.1.2
     hooks:
       - id: doc8
         args: ["--ignore=D001", "-q"]
         additional_dependencies:
           - importlib_metadata<5.0.0
 
   - repo: https://github.com/kynan/nbstripout
-    rev: 0.3.8
+    rev: 0.7.1
     hooks:
       - id: nbstripout
         files: ".ipynb"
 
-  - repo: https://github.com/prettier/pre-commit
-    rev: v2.1.2
+  - repo: https://github.com/pre-commit/mirrors-prettier
+    rev: v4.0.0-alpha.8
     hooks:
       - id: prettier
 
-  - repo: https://github.com/pre-commit/mirrors-isort
-    rev: v5.8.0
+  - repo: https://github.com/PyCQA/isort
+    rev: 5.13.2
     hooks:
       - id: isort
 
@@ -59,8 +59,11 @@ repos:
         types: [python]
         args: ["--rcfile=.pylintrc", "--score=no"]
 
-  - repo: https://github.com/myint/rstcheck
-    rev: 3f92957 # Use the sha / tag you want to point at
+  - repo: https://github.com/rstcheck/rstcheck
+    rev: v6.2.4 # Use the sha / tag you want to point at
     hooks:
       - id: rstcheck
         files: \.(inc|rst)$
+
+ci:
+  skip: [pylint]

checks/resolution/README.md

@@ -16,11 +16,16 @@ This is the toy distribution.
 The detector model, the reconstructed value is the truch value plus a gauss
 random number.
 
-$$ m' = m + \delta ,\ \delta \sim \frac{1}{\sqrt{2\pi}\sigma} \exp(-\frac{(\delta-\mu)^2}{2\sigma^2})$$
+<!-- prettier-ignore -->
+$$
+m' = m + \delta ,\ \delta \sim \frac{1}{\sqrt{2\pi}\sigma}
+\exp(-\frac{(\delta-\mu)^2}{2\sigma^2})
+$$
 
 There is also a simple linear efficency with a mass range cut for reconstucted
 value.
 
+<!-- prettier-ignore -->
 $$ \epsilon(m, m') = m \theta(m'-m_{min}) \theta(m_{max}-m') ,$$
 
 $\theta(x)$ is the step function.
@@ -39,7 +44,11 @@ Analytical form of truth and reconstructed value distribution.
 
 The function of detector.py is such transition funtion.
 
-$$ T(m, m') = \frac{1}{\sqrt{2\pi}\sigma}\exp(-\frac{(m'-m-\mu)^2}{2\sigma^2}) \epsilon(m, m') \rho(m) .$$
+<!-- prettier-ignore -->
+$$
+T(m, m') = \frac{1}{\sqrt{2\pi}\sigma}\exp(-\frac{(m'-m-\mu)^2}{2\sigma^2})
+\epsilon(m, m') \rho(m) .
+$$
 
 $\rho(m)=pq$ is the phase space factor, $p$, $q$ is the breakup monmentum of
 $A\rightarrow (BC) + D$ and $(BC) \rightarrow B + C$.
@@ -51,15 +60,21 @@ effect of efficency, we can set $\epsilon(m,m')=1$.
 
 The projections of the truth and reconstucted value distrubution are
 
+<!-- prettier-ignore -->
 $$ f_{truth}(m) = \int T(m, m') dm',$$
 
+<!-- prettier-ignore -->
 $$ f_{rec}(m') = \int T(m, m') dm,$$
 
 ![img](./assets/m_rec.png)
 
 and
 
-$$ f_{delta}(\delta) = \int T(\frac{m_s - \delta}{2}, \frac{m_s + \delta}{2}) d m_s.$$
+<!-- prettier-ignore -->
+$$
+f_{delta}(\delta) = \int T(\frac{m_s - \delta}{2}, \frac{m_s + \delta}{2})
+d m_s.
+$$
 
 ![img](./assets/m_diff.png)
 
@@ -69,6 +84,7 @@ It is a little different from gauss function because of the range cut.
 
 Generate truth values for resolution. The resolution function is defined as
 
+<!-- prettier-ignore -->
 $$ R(m \rightarrow m') = \frac{T(m, m')}{\int T(m,m') d m} .$$
 
 For every reconstructed values $m'$ (N events), we generate some $m_i$ (M
@@ -90,15 +106,27 @@ $x_i, A_i$ is sample points and weights for Gauss-Legendre quadrature.
 
 The probobility of reconstructed value is
 
-$$ P(m') = \int |A|^2 (m) T(m, m') dm \approx \epsilon_{rec}(m') \sum w_i' |A|^2(m_i). $$
+<!-- prettier-ignore -->
+$$
+P(m') = \int |A|^2 (m) T(m, m') dm \approx \epsilon_{rec}(m') \sum w_i'
+|A|^2(m_i).
+$$
 
 The normalised factor is
 
-$$ \int P(m') d\Phi = \int |A|^2 (m) \int T(m, m') dm' dm \approx \sum_{m\sim f_{truth}(m)} |A|^2(m), $$
+<!-- prettier-ignore -->
+$$
+\int P(m') d\Phi = \int |A|^2 (m) \int T(m, m') dm' dm \approx \sum_{m\sim
+f_{truth}(m)} |A|^2(m),
+$$
 
 The negative log-likelihood (NLL) value is
 
-$$ -\ln L = - \sum \ln P(m_j') + N \ln \int P(m_i') d \Phi \approx - \sum_{j} \ln \sum w_i' |A|(m_{ij}) + N \ln \int P(m_i') d \Phi + constant.$$
+<!-- prettier-ignore -->
+$$
+-\ln L = - \sum \ln P(m_j') + N \ln \int P(m_i') d \Phi \approx - \sum_{j}
+\ln \sum w_i' |A|(m_{ij}) + N \ln \int P(m_i') d \Phi + constant.
+$$
 
 This is fit result from one test.
 

setup.cfg

@@ -114,5 +114,8 @@ quiet=1
 
 [rstcheck]
 ignore_directives =
-    automodule
-report=warning
+    automodule,
+    toctree,
+    seealso,
+report_level=WARNING
+ignore_roles=ref,file,doc,

tf_pwa/adaptive_bins.py

@@ -81,8 +81,9 @@ def single_split_bound(data, n=2, base_bound=None):
         """split data in the order of data value
 
         >>> data = np.array([1.0, 2.0, 1.4, 3.1])
-        >>> AdaptiveBound.single_split_bound(data)
-        [(1.0, 1.7...), (1.7..., 3.1...)]
+        >>> bound = AdaptiveBound.single_split_bound(data)
+        >>> [(float(i[0]+1e-6), float(i[1]+1e-6)) for i in bound]
+        [(1.0..., 1.7...), (1.7..., 3.1...)]
 
         """
         if base_bound is None:
@@ -102,7 +103,7 @@ def multi_split_bound(datas, n, base_bound=None):
 
         >>> data = np.array([[1.0, 2.0, 1.4, 3.1], [2.0, 1.0, 3.0, 1.0]])
         >>> bound, _ = AdaptiveBound.multi_split_bound(data, [2, 1])
-        >>> [(i[0][0]+1e-6, i[1][0]+1e-6) for i in bound]
+        >>> [(float(i[0][0]+1e-6), float(i[1][0]+1e-6)) for i in bound]
         [(1.0..., 1.7...), (1.7..., 3.1...)]
 
         """

tf_pwa/amp/base.py

@@ -2,7 +2,6 @@
 Basic amplitude model
 """
 
-
 import numpy as np
 
 from tf_pwa.breit_wigner import (

tf_pwa/amp/interpolation.py

@@ -400,6 +400,7 @@ def get_matrix_interp1d3(x, xi):
     N = len(xi) - 1
     zeros = tf.zeros_like(x)
     ones = tf.ones_like(x)
+
     # @pysnooper.snoop()
     def poly_i(i):
         tmp = zeros
@@ -620,6 +621,7 @@ def get_matrix_interp1d3_v2(x, xi):
     N = len(xi) - 1
     zeros = tf.zeros_like(x)
     ones = tf.ones_like(x)
+
     # @pysnooper.snoop()
     def poly_i(i):
         tmp = zeros

tf_pwa/angle.py

@@ -1,6 +1,7 @@
 """
 This module implements three classes **Vector3**, **LorentzVector**, **EulerAngle** .
 """
+
 from .tensorflow_wrapper import numpy_cross, tf
 
 _epsilon = 1.0e-14

tf_pwa/applications.py

@@ -4,6 +4,7 @@
 In general, users only need to import functions in this module to implement their physical analysis instead of
 going into every modules. There are some example files where you can figure out how it is used.
 """
+
 import os
 import time
 import warnings

tf_pwa/cal_angle.py

@@ -49,6 +49,7 @@
 Inner nodes are named as tuple of particles.
 
 """
+
 import itertools
 
 import numpy as np

tf_pwa/dec_parser.py

@@ -1,6 +1,7 @@
 """
 module for parsing decay card :code:`*.dec` file
 """
+
 import re
 import warnings
 

tf_pwa/experimental/factor_system.py

@@ -20,6 +20,7 @@
 We can add some options to change the possible combination. (TODO)
 
 """
+
 import contextlib
 import itertools
 

tf_pwa/fit.py

@@ -39,7 +39,6 @@ def fit_minuit(fcn, bounds_dict={}, hesse=True, minos=False, **kwargs):
 
 
 def fit_minuit_v1(fcn, bounds_dict={}, hesse=True, minos=False, **kwargs):
-
     """
 
     :param fcn:
@@ -91,7 +90,6 @@ def fit_minuit_v1(fcn, bounds_dict={}, hesse=True, minos=False, **kwargs):
 
 
 def fit_minuit_v2(fcn, bounds_dict={}, hesse=True, minos=False, **kwargs):
-
     """
 
     :param fcn:
@@ -226,6 +224,7 @@ def fit_scipy(
         maxiter = max(100 * len(x0), 2000)
     min_nll = 0.0
     ndf = fcn.vm.get_all_val(True)
+
     # maxiter = 0
     def v_g2(x0):
         f_g = fcn.vm.trans_fcn_grad(fcn.nll_grad)

tf_pwa/fit_improve.py

@@ -98,7 +98,7 @@ def fmin_bfgs_f(
     Delta=10.0,
     maxiter=None,
     callback=None,
-    norm_ord=np.Inf,
+    norm_ord=np.inf,
     **_kwargs
 ):
     """test BFGS with nonmonote line search"""

tf_pwa/formula.py

@@ -29,9 +29,7 @@ def get_relative_p(ma, mb, mc):
 
 
 def get_relative_p2(ma, mb, mc):
-    return (
-        ((ma**2 - (mb + mc) ** 2) * (ma**2 - (mb - mc) ** 2)) / 4 / ma / ma
-    )
+    return ((ma**2 - (mb + mc) ** 2) * (ma**2 - (mb - mc) ** 2)) / 4 / ma / ma
 
 
 def BW_dom(m, m0, g0):
@@ -63,9 +61,7 @@ def BWR_LS_dom(m, m0, g0, thetas, ls, m1, m2, d=3.0, fix_bug1=False):
     p0 = get_relative_p2(m0, m1, m2)
 
     def bf_f(l):
-        bf = Bprime_polynomial(l, p0 * d**2) / Bprime_polynomial(
-            l, p * d**2
-        )
+        bf = Bprime_polynomial(l, p0 * d**2) / Bprime_polynomial(l, p * d**2)
         return (p / p0) ** l * bf
 
     g_head = sympy.I * m0 * g0 * m / m0 * sympy.sqrt(p / p0)

tf_pwa/generator/plane_2d.py

@@ -26,8 +26,7 @@ def _solve_2(a1, a2, x0, y):
     from numpy import sqrt
 
     return (
-        -a1
-        + sqrt(a1**2 + 4 * a1 * a2 * x0 + 4 * a2**2 * x0**2 + 4 * a2 * y)
+        -a1 + sqrt(a1**2 + 4 * a1 * a2 * x0 + 4 * a2**2 * x0**2 + 4 * a2 * y)
     ) / (2 * a2)
 
 

tf_pwa/model/model.py

@@ -1460,7 +1460,6 @@ def grad_hessp(self, x, p, batch=None):
 
 
 class MixLogLikehoodFCN(CombineFCN):
-
     """
     This class implements methods to calculate the NLL as well as its derivatives for a general function.
 

tf_pwa/model/opt_int.py

@@ -550,8 +550,7 @@ def grad_hessp_batch(self, p, data, mcdata, weight, mc_weight):
 
         g_int_mc = np.array(g_int_mc)
         hessp2 = sw * (
-            hessp_int_mc / int_mc
-            - g_int_mc * np.dot(p, g_int_mc) / int_mc**2
+            hessp_int_mc / int_mc - g_int_mc * np.dot(p, g_int_mc) / int_mc**2
         )
         # print("hessp2", hessp2)
         # print("ret", g, hessp2 - hessp_ln_data)

tf_pwa/particle.py

@@ -1,6 +1,7 @@
 """
 This module implements classes to describe particles and decays.
 """
+
 import functools
 from collections import UserList
 

tf_pwa/utils.py

@@ -1,6 +1,7 @@
 """
 This module provides some functions that may be useful in other modules.
 """
+
 import functools
 import json
 import math

tf_pwa/variable.py

@@ -1,6 +1,7 @@
 """
 This module implements classes and methods to manage the variables in fitting.
 """
+
 import contextlib
 import warnings
 

tutorials/examples/adaptive_chi2.py

@@ -23,9 +23,7 @@ def cal_chi2_config(
         data_cut = np.array([data_index(data, idx) for idx in data_idx])
     amp_weight = config.get_amplitude()(phsp).numpy()
     phsp_cut = np.array([data_index(phsp, idx) for idx in data_idx])
-    phsp_slice = np.concatenate(
-        [np.array(phsp_cut**2), [amp_weight]], axis=0
-    )
+    phsp_slice = np.concatenate([np.array(phsp_cut**2), [amp_weight]], axis=0)
     phsps = adapter.split_data(phsp_slice)
     datas = adapter.split_data(data_cut**2)
     bound = adapter.get_bounds()

tutorials/examples/check_interf.py

@@ -8,7 +8,6 @@
 
 """
 
-
 import numpy as np
 import tensorflow as tf